{"source_row": 0, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {830: [(711, 86400.0), (702, 43200.0)], 711: [(968, 129600.0)], 702: [(968, 129600.0)], 968: [(39, 43200.0)], 39: [(708, 0.0)], 708: [], 947: [(830, 259200.0)]}\nsource = 947\ntarget = 708\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 518400.0, "source_answer": 518400.0}
{"source_row": 1, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {540: [(928, 5.0)], 838: [(823, 15.0)], 823: [(968, 5.0)], 968: [(928, 5.0)], 928: [(857, 0.0)], 857: [], 666: [(540, 15.0), (838, 60.0)]}\nsource = 666\ntarget = 857\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 2, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {717: [(689, 15.0)], 689: [(906, 180.0), (75, 240.0)], 75: [(245, 15.0)], 906: [(245, 15.0)], 245: [(650, 0.0)], 650: [], 562: [(717, 15.0)]}\nsource = 562\ntarget = 650\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 285.0, "source_answer": 285.0}
{"source_row": 3, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {395: [(249, 5.0)], 249: [(767, 5.0)], 767: [(333, 15.0)], 923: [(249, 5.0)], 333: [(855, 0.0)], 855: [], 139: [(395, 10.0), (923, 1440.0)]}\nsource = 139\ntarget = 855\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1465.0, "source_answer": 1465.0}
{"source_row": 4, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {829: [(186, 15.0)], 235: [(397, 1.0)], 397: [(186, 15.0)], 186: [(377, 1.0)], 377: [(732, 10.0)], 732: [(944, 0.0)], 944: [], 739: [(829, 5.0), (235, 5.0)]}\nsource = 739\ntarget = 944\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 5, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {268: [(169, 10.0)], 242: [(169, 10.0)], 169: [(404, 15.0)], 404: [(513, 10.0)], 513: [(80, 0.0)], 80: [], 901: [(268, 5.0), (242, 5.0)]}\nsource = 901\ntarget = 80\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 6, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {346: [(549, 10.0)], 549: [(227, 20.0)], 227: [(973, 10.0)], 973: [(63, 1.0), (308, 3.0)], 308: [(407, 0.0)], 63: [(407, 0.0)], 407: [], 753: [(346, 20.0)]}\nsource = 753\ntarget = 407\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63.0, "source_answer": 63.0}
{"source_row": 7, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {448: [(509, 5.0), (129, 1.0)], 129: [(234, 0.08)], 509: [(366, 0.0)], 234: [(286, 1.0)], 286: [(366, 0.0)], 366: [], 686: [(448, 1.0)]}\nsource = 686\ntarget = 366\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 8, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {490: [(761, 43200.0)], 829: [(671, 525600.0)], 671: [(13, 86400.0)], 13: [(36, 0.0)], 761: [(547, 2102400.0), (829, 2102400.0)], 547: [(671, 525600.0)], 36: [], 82: [(490, 360.0)]}\nsource = 82\ntarget = 36\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2757960.0, "source_answer": 2757960.0}
{"source_row": 9, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {307: [(522, 5.0), (311, 2.0)], 522: [(768, 10.0)], 311: [(768, 10.0)], 768: [(609, 40.0)], 609: [(740, 0.0)], 740: [], 259: [(307, 15.0)]}\nsource = 259\ntarget = 740\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 10, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {698: [(340, 15.0)], 345: [(340, 15.0)], 340: [(675, 30.0)], 675: [(55, 5.0)], 55: [(614, 30.0)], 614: [(826, 0.0)], 826: [], 284: [(698, 15.0), (345, 30.0)]}\nsource = 284\ntarget = 826\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 110.0, "source_answer": 110.0}
{"source_row": 11, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {424: [(351, 0.33), (228, 0.33)], 351: [(586, 2.0)], 228: [(586, 2.0)], 586: [(306, 2.0)], 306: [(654, 0.0)], 654: [], 929: [(424, 0.33)]}\nsource = 929\ntarget = 654\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.66, "source_answer": 4.66}
{"source_row": 12, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(560, 43200.0), (377, 20160.0)], 560: [(917, 10080.0)], 377: [(917, 10080.0)], 917: [(108, 43200.0)], 108: [(709, 0.0)], 709: [], 391: [(505, 2102400.0)]}\nsource = 391\ntarget = 709\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2198880.0, "source_answer": 2198880.0}
{"source_row": 13, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {871: [(919, 5.0), (597, 35.0)], 919: [(946, 5.0)], 597: [(946, 5.0)], 946: [(77, 480.0)], 77: [(787, 0.0)], 787: [], 864: [(871, 60.0)]}\nsource = 864\ntarget = 787\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 580.0, "source_answer": 580.0}
{"source_row": 14, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {364: [(398, 1.0), (256, 5.0)], 398: [(130, 60.0)], 256: [(130, 60.0)], 130: [(693, 10.0)], 693: [(887, 0.0)], 887: [], 27: [(364, 1440.0)]}\nsource = 27\ntarget = 887\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1515.0, "source_answer": 1515.0}
{"source_row": 15, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {512: [(301, 5.0)], 357: [(301, 5.0)], 301: [(194, 15.0)], 194: [(999, 2.0)], 999: [(621, 10.0)], 621: [(86, 0.0)], 86: [], 215: [(512, 15.0), (357, 5.0)]}\nsource = 215\ntarget = 86\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 47.0, "source_answer": 47.0}
{"source_row": 16, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {213: [(341, 518400.0), (612, 518400.0)], 341: [(297, 1051200.0)], 612: [(297, 1051200.0)], 297: [(405, 1051200.0)], 405: [(685, 0.0)], 685: [], 76: [(213, 60.0)]}\nsource = 76\ntarget = 685\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2620860.0, "source_answer": 2620860.0}
{"source_row": 17, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {90: [(149, 10.0), (340, 10.0)], 149: [(375, 129600.0)], 340: [(375, 129600.0)], 375: [(589, 10080.0)], 589: [(926, 0.0)], 926: [], 382: [(90, 10.0)]}\nsource = 382\ntarget = 926\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 139700.0, "source_answer": 139700.0}
{"source_row": 18, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {67: [(45, 120.0), (293, 259200.0)], 45: [(429, 259200.0)], 293: [(429, 259200.0)], 429: [(983, 180.0), (195, 180.0)], 195: [(156, 360.0)], 983: [(156, 360.0)], 156: [(74, 0.0)], 74: [], 205: [(67, 86400.0)]}\nsource = 205\ntarget = 74\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 605340.0, "source_answer": 605340.0}
{"source_row": 19, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(615, 10.0), (419, 10.0)], 615: [(382, 30.0)], 419: [(382, 30.0)], 382: [(938, 10.0)], 938: [(793, 10.0)], 793: [(116, 86400.0)], 116: [(316, 0.0)], 316: [], 432: [(589, 25.0)]}\nsource = 432\ntarget = 316\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86485.0, "source_answer": 86485.0}
{"source_row": 20, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {83: [(912, 1.0)], 912: [(29, 129600.0), (824, 129600.0)], 824: [(122, 86400.0)], 29: [(122, 86400.0)], 122: [(650, 0.0)], 650: [], 439: [(83, 1.0)]}\nsource = 439\ntarget = 650\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 216002.0, "source_answer": 216002.0}
{"source_row": 21, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {628: [(685, 10.0)], 685: [(145, 10.0)], 145: [(816, 15.0)], 816: [(704, 5.0), (326, 2.0)], 326: [(977, 0.0)], 704: [(977, 0.0)], 977: [], 465: [(628, 15.0)]}\nsource = 465\ntarget = 977\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 22, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {830: [(911, 60.0)], 901: [(504, 1440.0)], 504: [(940, 0.0)], 911: [(808, 30.0)], 808: [(504, 1440.0)], 940: [], 791: [(830, 15.0), (901, 120.0)]}\nsource = 791\ntarget = 940\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1560.0, "source_answer": 1560.0}
{"source_row": 23, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {472: [(675, 60.0)], 900: [(326, 15.0)], 326: [(45, 15.0)], 45: [(675, 60.0)], 675: [(102, 0.0)], 102: [], 587: [(472, 15.0), (900, 1.0)]}\nsource = 587\ntarget = 102\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 91.0, "source_answer": 91.0}
{"source_row": 24, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {606: [(438, 25.0)], 627: [(438, 25.0)], 438: [(189, 10.0)], 189: [(182, 15.0)], 182: [(299, 0.0)], 299: [], 81: [(606, 1.0), (627, 5.0)]}\nsource = 81\ntarget = 299\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 25, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {462: [(243, 3.0)], 243: [(274, 5.0)], 274: [(879, 5.0), (378, 2.0)], 879: [(53, 4.0)], 378: [(53, 4.0)], 53: [(802, 0.0)], 802: [], 881: [(462, 5.0)]}\nsource = 881\ntarget = 802\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 26, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {31: [(687, 1.0)], 687: [(722, 60.0), (377, 60.0)], 377: [(436, 120.0)], 722: [(436, 120.0)], 436: [(219, 1576800.0)], 219: [(889, 0.0)], 889: [], 178: [(31, 120.0)]}\nsource = 178\ntarget = 889\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1577101.0, "source_answer": 1577101.0}
{"source_row": 27, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {53: [(337, 120.0)], 547: [(337, 120.0)], 337: [(433, 172800.0)], 433: [(229, 172800.0)], 229: [(429, 1440.0)], 429: [(3, 0.0)], 3: [], 867: [(53, 1440.0), (547, 4320.0)]}\nsource = 867\ntarget = 3\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 351480.0, "source_answer": 351480.0}
{"source_row": 28, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {209: [(10, 25.0), (394, 10.0)], 10: [(863, 5.0)], 394: [(863, 5.0)], 863: [(193, 1.0)], 193: [(139, 2.0)], 139: [(987, 0.0)], 987: [], 667: [(209, 5.0)]}\nsource = 667\ntarget = 987\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 38.0, "source_answer": 38.0}
{"source_row": 29, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {95: [(452, 30.0)], 378: [(859, 60.0)], 859: [(86, 15.0)], 86: [(21, 0.0)], 452: [(3, 15.0)], 3: [(739, 60.0)], 739: [(21, 0.0)], 21: [], 159: [(95, 15.0), (378, 60.0)]}\nsource = 159\ntarget = 21\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 30, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {997: [(436, 2.0)], 102: [(436, 2.0)], 436: [(560, 20.0)], 560: [(836, 0.0)], 292: [(436, 2.0)], 836: [], 869: [(997, 15.0), (102, 2.0), (292, 2.0)]}\nsource = 869\ntarget = 836\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 31, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {697: [(946, 10080.0)], 655: [(946, 10080.0)], 946: [(406, 10080.0)], 406: [(78, 4320.0)], 78: [(957, 0.0)], 957: [], 855: [(697, 1440.0), (655, 1440.0)]}\nsource = 855\ntarget = 957\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25920.0, "source_answer": 25920.0}
{"source_row": 32, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {751: [(577, 1440.0)], 316: [(577, 1440.0)], 577: [(83, 180.0)], 83: [(252, 43200.0)], 252: [(793, 1440.0), (483, 240.0)], 793: [(953, 0.0)], 483: [(953, 0.0)], 953: [], 236: [(751, 60.0), (316, 4320.0)]}\nsource = 236\ntarget = 953\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50580.0, "source_answer": 50580.0}
{"source_row": 33, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {174: [(469, 2.0)], 469: [(888, 3.0)], 888: [(671, 0.5)], 496: [(595, 0.5)], 595: [(888, 3.0)], 671: [(646, 0.0)], 646: [], 540: [(174, 0.5), (496, 1.0)]}\nsource = 540\ntarget = 646\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 34, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {240: [(520, 5.0), (171, 5.0), (429, 5.0)], 520: [(694, 30.0)], 429: [(694, 30.0)], 171: [(694, 30.0)], 694: [(133, 30.0)], 133: [(488, 45.0)], 488: [(202, 0.0)], 202: [], 229: [(240, 2.0)]}\nsource = 229\ntarget = 202\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 112.0, "source_answer": 112.0}
{"source_row": 35, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {13: [(117, 1.0)], 117: [(528, 1.0)], 528: [(16, 60.0)], 526: [(117, 1.0)], 16: [(782, 0.0)], 782: [], 609: [(13, 30.0), (526, 60.0)]}\nsource = 609\ntarget = 782\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 122.0, "source_answer": 122.0}
{"source_row": 36, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {41: [(717, 4320.0)], 147: [(717, 4320.0)], 717: [(684, 4320.0)], 684: [(595, 0.5)], 595: [(855, 0.0)], 855: [], 110: [(41, 1051200.0), (147, 1051200.0)]}\nsource = 110\ntarget = 855\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1059840.5, "source_answer": 1059840.5}
{"source_row": 37, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {273: [(343, 1.0)], 343: [(869, 0.08)], 869: [(87, 2.0), (443, 0.03)], 87: [(16, 0.08)], 443: [(16, 0.08)], 16: [(406, 0.0)], 406: [], 804: [(273, 10.0)]}\nsource = 804\ntarget = 406\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.16, "source_answer": 13.16}
{"source_row": 38, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {259: [(119, 15.0)], 119: [(112, 35.0)], 112: [(595, 15.0)], 595: [(53, 45.0), (605, 60.0)], 605: [(172, 15.0)], 53: [(172, 15.0)], 172: [(330, 0.0)], 330: [], 459: [(259, 120.0)]}\nsource = 459\ntarget = 330\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 260.0, "source_answer": 260.0}
{"source_row": 39, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {485: [(673, 15.0)], 837: [(673, 15.0)], 673: [(487, 5.0)], 487: [(428, 1.0)], 428: [(840, 1.0)], 840: [(865, 0.0)], 865: [], 992: [(485, 5.0), (837, 5.0)]}\nsource = 992\ntarget = 865\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 40, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {125: [(263, 30.0)], 741: [(263, 30.0)], 263: [(878, 15.0)], 878: [(649, 10.0)], 649: [(810, 0.0)], 810: [], 571: [(125, 15.0), (741, 5.0)]}\nsource = 571\ntarget = 810\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 41, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {476: [(833, 180.0)], 20: [(301, 0.0)], 946: [(301, 0.0)], 92: [(301, 0.0)], 833: [(92, 5256000.0), (946, 5256000.0), (20, 5256000.0)], 301: [], 839: [(476, 180.0)]}\nsource = 839\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5256360.0, "source_answer": 5256360.0}
{"source_row": 42, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {258: [(49, 15.0)], 49: [(896, 30.0)], 896: [(633, 15.0)], 633: [(875, 0.0)], 869: [(996, 15.0)], 996: [(49, 15.0)], 340: [(869, 60.0)], 875: [], 19: [(258, 60.0), (340, 15.0)]}\nsource = 19\ntarget = 875\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 43, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {499: [(621, 15.0)], 964: [(621, 15.0)], 621: [(226, 60.0)], 226: [(923, 15.0)], 923: [(269, 0.0)], 269: [], 540: [(499, 20.0), (964, 60.0)]}\nsource = 540\ntarget = 269\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 44, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {195: [(979, 15.0)], 979: [(659, 3.0), (39, 2.0)], 39: [(140, 2.0)], 659: [(140, 2.0)], 140: [(530, 0.0)], 530: [], 151: [(195, 2.0)]}\nsource = 151\ntarget = 530\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 45, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {936: [(325, 30.0)], 325: [(381, 43200.0)], 381: [(903, 0.0)], 567: [(768, 43200.0)], 768: [(381, 43200.0)], 903: [], 944: [(936, 15.0), (567, 10.0)]}\nsource = 944\ntarget = 903\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86410.0, "source_answer": 86410.0}
{"source_row": 46, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {670: [(196, 1.0)], 196: [(419, 0.17)], 419: [(39, 0.17)], 58: [(604, 0.0)], 39: [(604, 0.0)], 604: [], 40: [(670, 0.02), (58, 30.0)]}\nsource = 40\ntarget = 604\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 47, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {394: [(292, 2.0), (5, 1.0), (224, 1.0)], 292: [(607, 5.0)], 224: [(607, 5.0)], 5: [(607, 5.0)], 607: [(958, 5.0)], 958: [(65, 35.0)], 65: [(689, 0.0)], 689: [], 678: [(394, 1.0)]}\nsource = 678\ntarget = 689\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 48.0, "source_answer": 48.0}
{"source_row": 48, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {848: [(25, 15.0), (472, 10.0)], 25: [(299, 15.0)], 472: [(299, 15.0)], 299: [(326, 10.0)], 326: [(312, 0.0)], 312: [], 587: [(848, 5.0)]}\nsource = 587\ntarget = 312\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 49, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {508: [(834, 1.0)], 834: [(880, 1.0), (837, 1.0)], 837: [(765, 2.0)], 880: [(765, 2.0)], 765: [(542, 2.0)], 542: [(554, 2.0)], 554: [(249, 0.0)], 249: [], 247: [(508, 1.0)]}\nsource = 247\ntarget = 249\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 50, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(585, 2.0)], 161: [(87, 1.0)], 87: [(201, 1.0)], 201: [(846, 20.0)], 846: [(651, 1.0)], 585: [(201, 1.0)], 651: [(784, 0.0)], 784: [], 444: [(963, 1.0), (161, 2.0)]}\nsource = 444\ntarget = 784\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 51, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {56: [(283, 1440.0), (985, 10080.0)], 283: [(618, 10080.0)], 985: [(618, 10080.0)], 618: [(171, 1440.0)], 171: [(484, 0.0)], 484: [], 810: [(56, 10080.0)]}\nsource = 810\ntarget = 484\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31680.0, "source_answer": 31680.0}
{"source_row": 52, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {325: [(683, 60.0), (927, 30.0)], 683: [(669, 15.0)], 927: [(669, 15.0)], 669: [(296, 43200.0)], 296: [(158, 30.0)], 158: [(736, 0.0)], 736: [], 784: [(325, 10.0)]}\nsource = 784\ntarget = 736\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43315.0, "source_answer": 43315.0}
{"source_row": 53, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {898: [(531, 180.0)], 531: [(368, 60.0)], 368: [(371, 18720.0), (257, 1440.0)], 371: [(311, 120.0)], 257: [(311, 120.0)], 311: [(327, 0.0)], 327: [], 806: [(898, 180.0)]}\nsource = 806\ntarget = 327\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19260.0, "source_answer": 19260.0}
{"source_row": 54, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {473: [(443, 30.0)], 165: [(443, 30.0)], 443: [(989, 0.0)], 819: [(919, 5.0)], 919: [(443, 30.0)], 989: [], 925: [(473, 10080.0), (165, 30.0), (819, 20.0)]}\nsource = 925\ntarget = 989\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10110.0, "source_answer": 10110.0}
{"source_row": 55, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {210: [(991, 20.0)], 307: [(991, 20.0)], 991: [(502, 525600.0)], 502: [(480, 0.0)], 971: [(991, 20.0)], 11: [(991, 20.0)], 107: [(991, 20.0)], 480: [], 376: [(210, 15.0), (307, 5.0), (971, 5.0), (11, 15.0), (107, 15.0)]}\nsource = 376\ntarget = 480\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525635.0, "source_answer": 525635.0}
{"source_row": 56, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {411: [(348, 5.0), (684, 5.0)], 348: [(238, 2.0)], 684: [(238, 2.0)], 238: [(797, 1.0)], 797: [(446, 5.0)], 446: [(559, 5.0)], 559: [(868, 0.0)], 868: [], 600: [(411, 1.0)]}\nsource = 600\ntarget = 868\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19.0, "source_answer": 19.0}
{"source_row": 57, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {959: [(255, 20.0)], 255: [(65, 5.0), (632, 20.0)], 632: [(263, 5.0)], 65: [(263, 5.0)], 263: [(670, 20.0)], 670: [(309, 5.0)], 309: [(81, 0.0)], 81: [], 465: [(959, 20.0)]}\nsource = 465\ntarget = 81\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 58, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {926: [(529, 525600.0)], 528: [(529, 525600.0)], 529: [(939, 129600.0)], 939: [(424, 525600.0)], 424: [(415, 129600.0)], 415: [(744, 1051200.0)], 744: [(968, 0.0)], 968: [], 790: [(926, 129600.0), (528, 2102400.0)]}\nsource = 790\ntarget = 968\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4464000.0, "source_answer": 4464000.0}
{"source_row": 59, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {345: [(261, 30.0)], 89: [(669, 5.0)], 669: [(261, 30.0)], 261: [(18, 5.0)], 18: [(675, 0.0)], 675: [], 696: [(345, 60.0), (89, 15.0)]}\nsource = 696\ntarget = 675\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.0, "source_answer": 95.0}
{"source_row": 60, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {982: [(837, 1.0)], 421: [(186, 1.0)], 186: [(837, 1.0)], 837: [(732, 0.17)], 732: [(863, 60.0)], 46: [(837, 1.0)], 863: [(314, 0.0)], 314: [], 715: [(982, 5.0), (421, 60.0), (46, 60.0)]}\nsource = 715\ntarget = 314\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 122.17, "source_answer": 122.17}
{"source_row": 61, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {713: [(463, 15.0)], 770: [(463, 15.0)], 463: [(46, 30.0)], 46: [(698, 15.0)], 698: [(16, 120.0), (400, 60.0)], 16: [(480, 0.0)], 400: [(480, 0.0)], 480: [], 875: [(713, 60.0), (770, 60.0)]}\nsource = 875\ntarget = 480\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 62, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {409: [(272, 120.0)], 272: [(928, 240.0), (634, 240.0)], 634: [(275, 2880.0)], 928: [(856, 2880.0)], 275: [(192, 0.0)], 856: [(192, 0.0)], 192: [], 87: [(409, 1440.0)]}\nsource = 87\ntarget = 192\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4680.0, "source_answer": 4680.0}
{"source_row": 63, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {425: [(294, 4320.0)], 135: [(294, 4320.0)], 294: [(799, 10080.0)], 799: [(909, 120.0)], 909: [(242, 0.0)], 242: [], 37: [(425, 14400.0), (135, 28800.0)]}\nsource = 37\ntarget = 242\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43320.0, "source_answer": 43320.0}
{"source_row": 64, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {762: [(365, 5.0), (400, 5.0)], 365: [(830, 0.02)], 400: [(830, 0.02)], 830: [(850, 5.0)], 850: [(394, 0.0)], 394: [], 32: [(762, 0.02)]}\nsource = 32\ntarget = 394\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.04, "source_answer": 10.04}
{"source_row": 65, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {561: [(968, 4320.0)], 464: [(968, 4320.0)], 968: [(459, 0.0)], 595: [(968, 4320.0)], 991: [(968, 4320.0)], 52: [(968, 4320.0)], 720: [(968, 4320.0)], 459: [], 474: [(561, 4320.0), (464, 60.0), (595, 60.0), (991, 4320.0), (52, 4320.0), (720, 4320.0)]}\nsource = 474\ntarget = 459\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8640.0, "source_answer": 8640.0}
{"source_row": 66, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {605: [(543, 1.0)], 333: [(543, 1.0)], 543: [(526, 1.0)], 526: [(917, 1.0)], 63: [(543, 1.0)], 917: [(542, 0.25)], 542: [(258, 0.0)], 258: [], 500: [(605, 0.58), (333, 1.0), (63, 5.0)]}\nsource = 500\ntarget = 258\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.25, "source_answer": 8.25}
{"source_row": 67, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {41: [(284, 60.0)], 538: [(284, 60.0)], 284: [(397, 5.0)], 397: [(204, 5.0)], 104: [(284, 60.0)], 204: [(241, 0.0)], 241: [], 641: [(41, 30.0), (538, 30.0), (104, 30.0)]}\nsource = 641\ntarget = 241\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0}
{"source_row": 68, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {341: [(141, 15.0)], 816: [(141, 15.0)], 141: [(420, 1.0)], 420: [(578, 4.0)], 318: [(141, 15.0)], 578: [(206, 0.0)], 206: [], 425: [(341, 120.0), (816, 45.0), (318, 45.0)]}\nsource = 425\ntarget = 206\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 140.0, "source_answer": 140.0}
{"source_row": 69, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(784, 25.0)], 784: [(380, 0.0)], 736: [(380, 0.0)], 176: [(893, 7200.0)], 893: [(380, 0.0)], 303: [(380, 0.0)], 380: [], 76: [(589, 300.0), (736, 3.0), (176, 129600.0), (303, 30.0)]}\nsource = 76\ntarget = 380\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 136800.0, "source_answer": 136800.0}
{"source_row": 70, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {608: [(578, 5.0)], 795: [(737, 15.0)], 737: [(578, 5.0)], 578: [(271, 60.0)], 271: [(149, 180.0)], 149: [(152, 0.0)], 152: [], 968: [(608, 15.0), (795, 1.0)]}\nsource = 968\ntarget = 152\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 261.0, "source_answer": 261.0}
{"source_row": 71, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {974: [(63, 10.0)], 63: [(796, 10.0), (658, 240.0)], 658: [(744, 0.02)], 796: [(331, 60.0)], 744: [(711, 0.0)], 331: [(744, 0.02)], 711: [], 277: [(974, 0.5)]}\nsource = 277\ntarget = 711\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 250.52, "source_answer": 250.52}
{"source_row": 72, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {969: [(793, 30.0)], 97: [(934, 120.0)], 934: [(793, 30.0)], 793: [(150, 120.0)], 150: [(551, 0.0)], 698: [(793, 30.0)], 446: [(793, 30.0)], 551: [], 656: [(969, 240.0), (97, 60.0), (698, 120.0), (446, 120.0)]}\nsource = 656\ntarget = 551\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 390.0, "source_answer": 390.0}
{"source_row": 73, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {314: [(785, 25.0)], 785: [(389, 25.0), (756, 25.0)], 756: [(192, 25.0)], 389: [(192, 25.0)], 192: [(805, 4320.0)], 805: [(488, 0.0)], 488: [], 306: [(314, 15.0)]}\nsource = 306\ntarget = 488\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4410.0, "source_answer": 4410.0}
{"source_row": 74, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {219: [(101, 60.0)], 202: [(725, 60.0)], 725: [(851, 120.0)], 851: [(785, 15.0)], 785: [(359, 60.0)], 359: [(101, 60.0)], 101: [(6, 0.0)], 6: [], 248: [(219, 60.0), (202, 1.0)]}\nsource = 248\ntarget = 6\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 316.0, "source_answer": 316.0}
{"source_row": 75, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {8: [(167, 15.0)], 539: [(845, 15.0), (206, 15.0)], 206: [(79, 900.0)], 845: [(79, 900.0)], 79: [(167, 15.0)], 167: [(88, 15.0)], 88: [(772, 0.0)], 772: [], 468: [(8, 15.0), (539, 15.0)]}\nsource = 468\ntarget = 772\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 960.0, "source_answer": 960.0}
{"source_row": 76, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {619: [(788, 0.0)], 239: [(853, 60.0)], 853: [(788, 0.0)], 457: [(853, 60.0)], 486: [(788, 0.0)], 72: [(788, 0.0)], 953: [(788, 0.0)], 788: [], 80: [(619, 43200.0), (239, 5.0), (457, 60.0), (486, 60.0), (72, 60.0), (953, 5.0)]}\nsource = 80\ntarget = 788\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43200.0, "source_answer": 43200.0}
{"source_row": 77, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {615: [(501, 1440.0)], 501: [(49, 1440.0), (265, 1440.0)], 265: [(700, 180.0)], 49: [(700, 180.0)], 700: [(765, 0.0)], 765: [], 179: [(615, 1440.0)]}\nsource = 179\ntarget = 765\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4500.0, "source_answer": 4500.0}
{"source_row": 78, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {452: [(835, 20.0)], 106: [(802, 20.0)], 802: [(764, 20.0)], 764: [(835, 20.0)], 835: [(743, 0.0)], 942: [(764, 20.0)], 743: [], 730: [(452, 0.5), (106, 10.0), (942, 2.0)]}\nsource = 730\ntarget = 743\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 79, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {126: [(249, 15.0)], 249: [(675, 1440.0)], 675: [(15, 4320.0)], 15: [(304, 0.0)], 345: [(249, 15.0)], 304: [], 766: [(126, 30.0), (345, 20.0)]}\nsource = 766\ntarget = 304\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5805.0, "source_answer": 5805.0}
{"source_row": 80, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {628: [(481, 10.0)], 481: [(249, 30.0)], 249: [(502, 0.0)], 914: [(182, 30.0)], 182: [(481, 10.0)], 502: [], 916: [(628, 10.0), (914, 15.0)]}\nsource = 916\ntarget = 502\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 81, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {896: [(498, 15.0)], 336: [(328, 5.0), (498, 15.0)], 498: [(769, 5.0)], 328: [(145, 15.0)], 769: [(483, 0.0)], 145: [(769, 5.0)], 300: [(328, 5.0), (498, 15.0)], 483: [], 445: [(896, 1.0), (336, 2.0), (300, 4.0)]}\nsource = 445\ntarget = 483\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 29.0, "source_answer": 29.0}
{"source_row": 82, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {702: [(337, 3.0)], 337: [(739, 5.0)], 739: [(752, 60.0)], 752: [(43, 2.0), (725, 60.0)], 43: [(595, 180.0)], 595: [(579, 0.0)], 725: [(595, 180.0)], 579: [], 729: [(702, 60.0)]}\nsource = 729\ntarget = 579\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 368.0, "source_answer": 368.0}
{"source_row": 83, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {680: [(812, 240.0)], 876: [(812, 240.0)], 812: [(760, 14400.0)], 760: [(504, 14400.0)], 898: [(812, 240.0)], 504: [(818, 14400.0)], 818: [(635, 0.0)], 635: [], 899: [(680, 60.0), (876, 120.0), (898, 120.0)]}\nsource = 899\ntarget = 635\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43560.0, "source_answer": 43560.0}
{"source_row": 84, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {190: [(911, 0.17), (525, 0.17)], 911: [(123, 0.17)], 525: [(966, 0.17)], 123: [(967, 1.0)], 966: [(967, 1.0)], 967: [(732, 10.0)], 732: [(348, 0.0)], 348: [], 204: [(190, 1.0)]}\nsource = 204\ntarget = 348\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.34, "source_answer": 12.34}
{"source_row": 85, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {815: [(272, 60.0)], 272: [(400, 180.0)], 400: [(713, 60.0), (462, 60.0)], 713: [(783, 0.0)], 462: [(783, 0.0)], 783: [], 833: [(815, 60.0)]}\nsource = 833\ntarget = 783\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 360.0, "source_answer": 360.0}
{"source_row": 86, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {350: [(639, 2.0)], 386: [(99, 0.0)], 639: [(125, 10.0)], 125: [(386, 3.0), (800, 3.0)], 800: [(99, 0.0)], 99: [], 249: [(350, 5.0)]}\nsource = 249\ntarget = 99\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 87, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {757: [(832, 10.0)], 832: [(57, 5.0)], 57: [(330, 5.0), (420, 45.0)], 330: [(71, 15.0)], 420: [(71, 15.0)], 71: [(620, 0.0)], 620: [], 805: [(757, 3.0)]}\nsource = 805\ntarget = 620\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 78.0, "source_answer": 78.0}
{"source_row": 88, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {319: [(760, 45.0)], 878: [(760, 45.0)], 760: [(105, 240.0)], 105: [(156, 25.0)], 156: [(626, 0.0)], 626: [], 698: [(319, 15.0), (878, 15.0)]}\nsource = 698\ntarget = 626\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 325.0, "source_answer": 325.0}
{"source_row": 89, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {293: [(592, 1440.0), (618, 240.0)], 592: [(931, 360.0)], 618: [(931, 360.0)], 931: [(526, 1440.0)], 526: [(83, 240.0)], 83: [(416, 1440.0)], 416: [(335, 0.0)], 335: [], 743: [(293, 120.0)]}\nsource = 743\ntarget = 335\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5040.0, "source_answer": 5040.0}
{"source_row": 90, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {979: [(826, 2.0), (651, 0.5)], 826: [(258, 2.0)], 651: [(239, 45.0)], 258: [(239, 45.0)], 239: [(603, 15.0)], 603: [(119, 1.0)], 119: [(237, 0.0)], 237: [], 271: [(979, 1.0)]}\nsource = 271\ntarget = 237\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 66.0, "source_answer": 66.0}
{"source_row": 91, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {8: [(706, 1.0), (470, 1.0)], 909: [(573, 5.0)], 573: [(522, 15.0)], 522: [(826, 5.0)], 826: [(706, 1.0), (470, 1.0)], 706: [(358, 0.0)], 470: [(358, 0.0)], 358: [], 713: [(8, 5.0), (909, 10.0)]}\nsource = 713\ntarget = 358\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.0, "source_answer": 36.0}
{"source_row": 92, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {765: [(712, 20.0)], 377: [(712, 20.0)], 712: [(89, 20.0)], 89: [(345, 15.0)], 345: [(143, 0.0)], 143: [], 599: [(765, 10.0), (377, 10.0)]}\nsource = 599\ntarget = 143\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 93, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {200: [(677, 5.0)], 677: [(240, 15.0), (93, 25.0)], 93: [(26, 120.0)], 240: [(26, 120.0)], 26: [(573, 5.0)], 573: [(338, 0.0)], 338: [], 47: [(200, 15.0)]}\nsource = 47\ntarget = 338\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 170.0, "source_answer": 170.0}
{"source_row": 94, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {11: [(493, 30.0), (510, 60.0)], 493: [(993, 20.0)], 993: [(916, 10.0)], 916: [(134, 0.0)], 510: [(993, 20.0)], 134: [], 809: [(11, 30.0)]}\nsource = 809\ntarget = 134\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 95, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {665: [(353, 30.0)], 841: [(353, 30.0)], 353: [(8, 10.0)], 8: [(909, 30.0)], 909: [(121, 30.0)], 121: [(794, 0.0)], 794: [], 314: [(665, 60.0), (841, 120.0)]}\nsource = 314\ntarget = 794\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 220.0, "source_answer": 220.0}
{"source_row": 96, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {55: [(715, 1.0)], 773: [(379, 1.0)], 379: [(779, 5.0)], 779: [(604, 1.0)], 604: [(404, 3.0)], 404: [(950, 0.0)], 715: [(604, 1.0)], 950: [], 214: [(55, 1.0), (773, 1.0)]}\nsource = 214\ntarget = 950\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 97, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {897: [(111, 1440.0)], 252: [(31, 1.0)], 31: [(597, 60.0)], 597: [(460, 10.0)], 460: [(524, 5.0)], 111: [(524, 5.0)], 524: [(205, 0.0)], 205: [], 40: [(897, 5.0), (252, 10.0)]}\nsource = 40\ntarget = 205\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1450.0, "source_answer": 1450.0}
{"source_row": 98, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {401: [(233, 60.0)], 665: [(950, 5.0)], 950: [(612, 2.0)], 612: [(233, 60.0)], 233: [(36, 5.0)], 36: [(107, 5.0)], 107: [(267, 0.0)], 267: [], 557: [(401, 5.0), (665, 5.0)]}\nsource = 557\ntarget = 267\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 82.0, "source_answer": 82.0}
{"source_row": 99, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {195: [(584, 120.0)], 584: [(620, 7200.0), (981, 7200.0)], 981: [(651, 30.0)], 620: [(651, 30.0)], 651: [(83, 0.0)], 83: [], 43: [(195, 20160.0)]}\nsource = 43\ntarget = 83\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27510.0, "source_answer": 27510.0}
{"source_row": 100, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {63: [(293, 10.0)], 383: [(293, 10.0)], 293: [(744, 2880.0)], 744: [(64, 120.0)], 64: [(297, 10.0)], 297: [(568, 0.0)], 568: [], 207: [(63, 60.0), (383, 60.0)]}\nsource = 207\ntarget = 568\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3080.0, "source_answer": 3080.0}
{"source_row": 101, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {990: [(49, 5.0)], 49: [(895, 5760.0), (56, 1440.0)], 56: [(916, 30.0)], 895: [(382, 0.0)], 916: [(382, 0.0)], 382: [], 191: [(990, 1440.0)]}\nsource = 191\ntarget = 382\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7205.0, "source_answer": 7205.0}
{"source_row": 102, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {716: [(930, 0.5)], 930: [(142, 5.0), (525, 5.0), (919, 60.0)], 525: [(231, 120.0)], 142: [(231, 120.0)], 919: [(231, 120.0)], 231: [(233, 0.0)], 233: [], 280: [(716, 45.0)]}\nsource = 280\ntarget = 233\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 225.5, "source_answer": 225.5}
{"source_row": 103, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {736: [(66, 60.0)], 218: [(680, 480.0)], 680: [(937, 600.0)], 937: [(754, 0.0)], 66: [(937, 600.0)], 754: [], 578: [(736, 30.0), (218, 15.0)]}\nsource = 578\ntarget = 754\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1095.0, "source_answer": 1095.0}
{"source_row": 104, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {794: [(114, 43200.0), (914, 129600.0)], 114: [(349, 60.0)], 349: [(154, 1440.0)], 154: [(665, 0.0)], 914: [(349, 60.0)], 665: [], 283: [(794, 30.0)]}\nsource = 283\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 131130.0, "source_answer": 131130.0}
{"source_row": 105, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {273: [(152, 3.0), (976, 1.0)], 152: [(897, 1.0)], 976: [(897, 1.0)], 897: [(418, 0.33)], 418: [(842, 1.0)], 842: [(460, 0.0)], 460: [], 372: [(273, 3.0)]}\nsource = 372\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.33, "source_answer": 8.33}
{"source_row": 106, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {176: [(733, 5.0)], 350: [(493, 15.0)], 493: [(367, 300.0)], 367: [(30, 5.0)], 918: [(367, 300.0)], 30: [(506, 0.0)], 733: [(367, 300.0)], 506: [], 314: [(176, 1.0), (350, 5.0), (918, 10.0)]}\nsource = 314\ntarget = 506\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 325.0, "source_answer": 325.0}
{"source_row": 107, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {895: [(378, 5.0), (321, 2.0), (997, 2.0)], 378: [(450, 5.0)], 997: [(450, 5.0)], 321: [(450, 5.0)], 450: [(645, 20.0)], 645: [(999, 5.0)], 999: [(175, 0.0)], 175: [], 687: [(895, 20.0)]}\nsource = 687\ntarget = 175\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 108, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {31: [(907, 60.0)], 4: [(144, 5.0)], 144: [(795, 15.0)], 795: [(772, 180.0)], 772: [(319, 0.0)], 907: [(694, 15.0)], 694: [(795, 15.0)], 319: [], 793: [(31, 15.0), (4, 15.0)]}\nsource = 793\ntarget = 319\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 285.0, "source_answer": 285.0}
{"source_row": 109, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {117: [(111, 15.0)], 111: [(787, 15.0), (138, 15.0)], 138: [(860, 15.0)], 787: [(860, 15.0)], 860: [(489, 150.0)], 489: [(401, 0.0)], 401: [], 573: [(117, 30.0)]}\nsource = 573\ntarget = 401\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 225.0, "source_answer": 225.0}
{"source_row": 110, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {117: [(956, 60.0)], 221: [(127, 10.0)], 127: [(956, 60.0)], 956: [(597, 420.0)], 597: [(34, 1051200.0), (573, 12000.0)], 34: [(466, 0.0)], 573: [(466, 0.0)], 466: [], 2: [(117, 120.0), (221, 300.0)]}\nsource = 2\ntarget = 466\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1051990.0, "source_answer": 1051990.0}
{"source_row": 111, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {307: [(380, 15.0), (614, 25.0)], 380: [(16, 15.0)], 614: [(16, 15.0)], 16: [(953, 120.0)], 953: [(726, 0.0)], 726: [], 891: [(307, 15.0)]}\nsource = 891\ntarget = 726\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 175.0, "source_answer": 175.0}
{"source_row": 112, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {692: [(554, 5.0)], 554: [(188, 1.0)], 188: [(594, 0.5)], 594: [(777, 0.25), (604, 0.25)], 604: [(765, 0.0)], 777: [(765, 0.0)], 765: [], 921: [(692, 1.0)]}\nsource = 921\ntarget = 765\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.75, "source_answer": 7.75}
{"source_row": 113, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {737: [(241, 2.0)], 241: [(460, 1.0)], 460: [(811, 0.5)], 811: [(5, 1.0)], 5: [(923, 0.17), (967, 0.5)], 923: [(185, 0.0)], 967: [(185, 0.0)], 185: [], 368: [(737, 2.0)]}\nsource = 368\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 114, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {380: [(755, 1440.0)], 755: [(438, 240.0), (150, 60.0)], 150: [(541, 10.0)], 438: [(541, 10.0)], 541: [(355, 0.0)], 355: [], 178: [(380, 60.0)]}\nsource = 178\ntarget = 355\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1750.0, "source_answer": 1750.0}
{"source_row": 115, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {594: [(234, 86400.0)], 234: [(891, 172800.0), (678, 216000.0)], 678: [(790, 525600.0)], 891: [(790, 525600.0)], 790: [(954, 0.0)], 954: [], 902: [(594, 43200.0)]}\nsource = 902\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 871200.0, "source_answer": 871200.0}
{"source_row": 116, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {492: [(619, 5.0)], 480: [(619, 5.0)], 619: [(829, 1.0)], 829: [(187, 2.0)], 187: [(804, 5.0)], 804: [(887, 1.0)], 887: [(983, 0.0)], 983: [], 216: [(492, 5.0), (480, 1.0)]}\nsource = 216\ntarget = 983\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19.0, "source_answer": 19.0}
{"source_row": 117, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {921: [(334, 1.0)], 334: [(412, 15.0)], 412: [(152, 5.0), (226, 15.0)], 152: [(491, 0.0)], 226: [(491, 0.0)], 491: [], 858: [(921, 10.0)]}\nsource = 858\ntarget = 491\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 118, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {716: [(168, 15.0)], 168: [(341, 5.0), (775, 5.0)], 775: [(407, 3.0)], 341: [(407, 3.0)], 407: [(863, 3.0)], 863: [(5, 0.0)], 5: [], 77: [(716, 15.0)]}\nsource = 77\ntarget = 5\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 119, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {645: [(749, 129600.0)], 178: [(467, 129600.0)], 467: [(749, 129600.0)], 749: [(404, 129600.0)], 404: [(661, 0.0)], 661: [], 824: [(645, 129600.0), (178, 60.0)]}\nsource = 824\ntarget = 661\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 388860.0, "source_answer": 388860.0}
{"source_row": 120, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {821: [(870, 1.0)], 870: [(940, 1.0), (264, 0.27)], 264: [(597, 1.0)], 940: [(645, 1.0)], 597: [(918, 0.0)], 645: [(304, 1.0)], 304: [(597, 1.0)], 918: [], 171: [(821, 1.0)]}\nsource = 171\ntarget = 918\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 121, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {33: [(867, 120.0)], 867: [(12, 120.0), (663, 120.0)], 663: [(923, 21600.0)], 12: [(813, 36000.0)], 813: [(447, 0.0)], 923: [(447, 0.0)], 447: [], 27: [(33, 60.0)]}\nsource = 27\ntarget = 447\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36300.0, "source_answer": 36300.0}
{"source_row": 122, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {609: [(185, 60.0)], 140: [(726, 60.0)], 726: [(185, 60.0)], 185: [(935, 1051200.0)], 935: [(204, 1440.0)], 204: [(130, 0.0)], 130: [], 794: [(609, 1440.0), (140, 1.0)]}\nsource = 794\ntarget = 130\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1054140.0, "source_answer": 1054140.0}
{"source_row": 123, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {730: [(38, 0.25)], 614: [(38, 0.25)], 38: [(715, 30.0)], 715: [(280, 10.0)], 280: [(149, 10.0)], 149: [(232, 15.0)], 232: [(509, 0.0)], 509: [], 96: [(730, 30.0), (614, 30.0)]}\nsource = 96\ntarget = 509\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.25, "source_answer": 95.25}
{"source_row": 124, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {60: [(941, 0.17)], 941: [(747, 0.17), (765, 0.17)], 765: [(903, 30.0)], 747: [(903, 30.0)], 903: [(615, 1.0)], 615: [(939, 0.17)], 939: [(4, 0.0)], 4: [], 905: [(60, 60.0)]}\nsource = 905\ntarget = 4\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 91.51, "source_answer": 91.51}
{"source_row": 125, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {438: [(316, 7200.0)], 316: [(263, 43200.0), (38, 7200.0), (446, 388800.0)], 38: [(699, 2102400.0)], 263: [(699, 2102400.0)], 446: [(699, 2102400.0)], 699: [(824, 0.0)], 824: [], 58: [(438, 129600.0)]}\nsource = 58\ntarget = 824\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2628000.0, "source_answer": 2628000.0}
{"source_row": 126, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {128: [(826, 5.0), (163, 15.0)], 826: [(20, 25.0)], 163: [(20, 25.0)], 20: [(122, 120.0)], 122: [(271, 0.0)], 271: [], 817: [(128, 5.0)]}\nsource = 817\ntarget = 271\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 165.0, "source_answer": 165.0}
{"source_row": 127, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {932: [(803, 240.0)], 803: [(367, 120.0), (779, 240.0)], 779: [(856, 120.0)], 367: [(856, 120.0)], 856: [(672, 129600.0)], 672: [(296, 0.0)], 296: [], 462: [(932, 480.0)]}\nsource = 462\ntarget = 296\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130680.0, "source_answer": 130680.0}
{"source_row": 128, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {533: [(826, 7200.0)], 896: [(416, 10.0)], 416: [(330, 180.0)], 330: [(826, 7200.0)], 826: [(455, 0.0)], 455: [], 903: [(533, 30.0), (896, 120.0)]}\nsource = 903\ntarget = 455\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7510.0, "source_answer": 7510.0}
{"source_row": 129, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {900: [(894, 15.0)], 108: [(321, 15.0)], 321: [(894, 15.0)], 894: [(996, 5.0)], 996: [(415, 15.0)], 415: [(19, 0.0)], 19: [], 389: [(900, 15.0), (108, 1.0)]}\nsource = 389\ntarget = 19\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 51.0, "source_answer": 51.0}
{"source_row": 130, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {451: [(944, 259200.0)], 944: [(352, 3153600.0)], 352: [(161, 259200.0), (340, 5256000.0)], 161: [(100, 0.0)], 340: [(100, 0.0)], 100: [], 855: [(451, 60.0)]}\nsource = 855\ntarget = 100\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8668860.0, "source_answer": 8668860.0}
{"source_row": 131, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {742: [(415, 35.0), (976, 20.0)], 401: [(58, 20.0)], 58: [(883, 0.0)], 415: [(401, 20.0)], 976: [(401, 20.0)], 883: [], 306: [(742, 5.0)]}\nsource = 306\ntarget = 883\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0}
{"source_row": 132, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {541: [(591, 1051200.0), (57, 1051200.0)], 591: [(639, 1051200.0)], 57: [(639, 1051200.0)], 639: [(381, 60.0)], 381: [(440, 0.0)], 440: [], 111: [(541, 60.0)]}\nsource = 111\ntarget = 440\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2102520.0, "source_answer": 2102520.0}
{"source_row": 133, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {202: [(902, 60.0)], 191: [(603, 43200.0)], 603: [(317, 0.0)], 902: [(191, 120.0), (344, 60.0)], 344: [(603, 43200.0)], 317: [], 376: [(202, 60.0)]}\nsource = 376\ntarget = 317\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43440.0, "source_answer": 43440.0}
{"source_row": 134, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(313, 60.0)], 313: [(315, 43200.0)], 315: [(781, 5.0), (525, 43200.0)], 781: [(412, 0.0)], 525: [(412, 0.0)], 412: [], 362: [(589, 1440.0)]}\nsource = 362\ntarget = 412\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 87900.0, "source_answer": 87900.0}
{"source_row": 135, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {645: [(738, 4320.0), (562, 4320.0)], 738: [(523, 259200.0)], 562: [(523, 259200.0)], 523: [(32, 43200.0)], 32: [(996, 86400.0)], 996: [(634, 0.0)], 634: [], 483: [(645, 60.0)]}\nsource = 483\ntarget = 634\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 393180.0, "source_answer": 393180.0}
{"source_row": 136, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {624: [(190, 3.0)], 190: [(908, 1.0)], 908: [(72, 0.0)], 430: [(501, 15.0)], 501: [(908, 1.0)], 72: [], 531: [(624, 0.5), (430, 0.5)]}\nsource = 531\ntarget = 72\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.5, "source_answer": 16.5}
{"source_row": 137, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {999: [(958, 7200.0), (241, 10.0)], 958: [(929, 64800.0)], 241: [(929, 64800.0)], 929: [(679, 120.0)], 679: [(369, 0.0)], 369: [], 567: [(999, 2880.0)]}\nsource = 567\ntarget = 369\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75000.0, "source_answer": 75000.0}
{"source_row": 138, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {676: [(644, 3.0)], 582: [(644, 3.0)], 644: [(92, 180.0)], 92: [(758, 5.0)], 758: [(275, 2880.0)], 275: [(306, 0.0)], 306: [], 261: [(676, 180.0), (582, 720.0)]}\nsource = 261\ntarget = 306\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3788.0, "source_answer": 3788.0}
{"source_row": 139, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {842: [(589, 0.0)], 15: [(686, 5.0)], 686: [(353, 20.0)], 353: [(185, 60.0)], 185: [(589, 0.0)], 589: [], 573: [(842, 10.0), (15, 1.0)]}\nsource = 573\ntarget = 589\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86.0, "source_answer": 86.0}
{"source_row": 140, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {125: [(558, 5.0)], 406: [(676, 5.0)], 676: [(27, 45.0)], 27: [(713, 15.0)], 713: [(172, 0.0)], 558: [(27, 45.0)], 172: [], 317: [(125, 30.0), (406, 20.0)]}\nsource = 317\ntarget = 172\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.0, "source_answer": 95.0}
{"source_row": 141, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {679: [(818, 5.0)], 818: [(619, 2.0), (904, 2.0)], 904: [(155, 0.5)], 619: [(155, 0.5)], 155: [(226, 0.08)], 226: [(891, 0.0)], 891: [], 449: [(679, 5.0)]}\nsource = 449\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.58, "source_answer": 12.58}
{"source_row": 142, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {241: [(550, 15.0)], 506: [(285, 3.0)], 285: [(550, 15.0)], 550: [(694, 15.0)], 904: [(550, 15.0)], 694: [(140, 480.0)], 140: [(779, 0.0)], 779: [], 69: [(241, 2.0), (506, 30.0), (904, 10.0)]}\nsource = 69\ntarget = 779\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 543.0, "source_answer": 543.0}
{"source_row": 143, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {753: [(322, 240.0), (220, 240.0)], 322: [(457, 120.0)], 220: [(457, 120.0)], 457: [(596, 5.0), (655, 60.0)], 655: [(347, 5.0)], 596: [(347, 5.0)], 347: [(883, 0.0)], 883: [], 750: [(753, 480.0)]}\nsource = 750\ntarget = 883\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 905.0, "source_answer": 905.0}
{"source_row": 144, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {303: [(636, 1.0)], 636: [(353, 0.17)], 353: [(403, 0.08)], 293: [(993, 3.0)], 993: [(403, 0.08)], 403: [(612, 0.08)], 612: [(190, 0.0)], 190: [], 662: [(303, 1.0), (293, 5.0)]}\nsource = 662\ntarget = 190\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.16, "source_answer": 8.16}
{"source_row": 145, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {850: [(743, 180.0)], 934: [(743, 180.0)], 743: [(767, 30.0)], 767: [(297, 180.0)], 297: [(371, 60.0)], 371: [(769, 10080.0)], 769: [(532, 0.0)], 532: [], 43: [(850, 86400.0), (934, 60.0)]}\nsource = 43\ntarget = 532\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 96930.0, "source_answer": 96930.0}
{"source_row": 146, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {834: [(237, 0.08)], 433: [(237, 0.08)], 237: [(31, 0.33)], 31: [(258, 0.08)], 258: [(184, 0.0)], 184: [], 611: [(834, 0.05), (433, 0.33)]}\nsource = 611\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.82, "source_answer": 0.82}
{"source_row": 147, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {271: [(487, 60.0)], 746: [(487, 60.0)], 487: [(37, 2.0)], 37: [(482, 5.0)], 482: [(548, 2880.0)], 548: [(5, 0.0)], 5: [], 464: [(271, 60.0), (746, 2.0)]}\nsource = 464\ntarget = 5\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3007.0, "source_answer": 3007.0}
{"source_row": 148, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {572: [(442, 3.0)], 442: [(922, 0.33), (994, 2.0)], 994: [(318, 0.17)], 922: [(318, 0.17)], 318: [(795, 0.0)], 795: [], 614: [(572, 3.0)]}\nsource = 614\ntarget = 795\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.17, "source_answer": 8.17}
{"source_row": 149, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {29: [(159, 3.0)], 111: [(626, 0.5)], 626: [(743, 20.0)], 743: [(265, 0.0)], 159: [(743, 20.0)], 265: [], 360: [(29, 0.33), (111, 2.0)]}\nsource = 360\ntarget = 265\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.33, "source_answer": 23.33}
{"source_row": 150, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {728: [(294, 10.0)], 294: [(751, 10.0)], 751: [(203, 20.0), (71, 20.0)], 203: [(429, 0.0)], 71: [(429, 0.0)], 429: [], 685: [(728, 30.0)]}\nsource = 685\ntarget = 429\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 151, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {78: [(463, 1.0)], 362: [(463, 1.0)], 463: [(837, 5.0)], 837: [(991, 10080.0)], 497: [(463, 1.0)], 991: [(127, 0.0)], 127: [], 14: [(78, 360.0), (362, 60.0), (497, 60.0)]}\nsource = 14\ntarget = 127\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10446.0, "source_answer": 10446.0}
{"source_row": 152, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {738: [(698, 0.02)], 110: [(336, 0.02)], 336: [(507, 0.05)], 507: [(518, 0.0)], 698: [(507, 0.05)], 518: [], 330: [(738, 0.03), (110, 0.03)]}\nsource = 330\ntarget = 518\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.1, "source_answer": 0.1}
{"source_row": 153, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {507: [(470, 480.0)], 934: [(470, 480.0)], 470: [(852, 60.0)], 852: [(895, 5760.0)], 895: [(940, 0.0)], 940: [], 819: [(507, 180.0), (934, 300.0)]}\nsource = 819\ntarget = 940\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6600.0, "source_answer": 6600.0}
{"source_row": 154, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {567: [(427, 5.0), (606, 15.0)], 427: [(802, 15.0)], 606: [(802, 15.0)], 802: [(646, 45.0)], 646: [(464, 0.0)], 464: [], 769: [(567, 15.0)]}\nsource = 769\ntarget = 464\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 155, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {164: [(781, 15.0)], 781: [(87, 10.0)], 87: [(88, 30.0)], 88: [(439, 0.5), (880, 3.0)], 880: [(982, 0.0)], 439: [(982, 0.0)], 982: [], 955: [(164, 15.0)]}\nsource = 955\ntarget = 982\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 73.0, "source_answer": 73.0}
{"source_row": 156, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {75: [(164, 10.0)], 798: [(553, 120.0)], 553: [(164, 10.0)], 164: [(785, 60.0)], 785: [(962, 5.0)], 962: [(57, 0.0)], 57: [], 618: [(75, 300.0), (798, 1.0)]}\nsource = 618\ntarget = 57\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 375.0, "source_answer": 375.0}
{"source_row": 157, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {547: [(599, 45.0)], 839: [(106, 25.0)], 106: [(90, 15.0)], 90: [(599, 45.0)], 599: [(287, 60.0)], 287: [(891, 0.0)], 891: [], 872: [(547, 15.0), (839, 15.0)]}\nsource = 872\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 160.0, "source_answer": 160.0}
{"source_row": 158, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {272: [(996, 525600.0), (504, 525600.0), (778, 525600.0), (77, 525600.0)], 77: [(742, 0.0)], 778: [(742, 0.0)], 504: [(742, 0.0)], 996: [(742, 0.0)], 742: [], 355: [(272, 720.0)]}\nsource = 355\ntarget = 742\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 526320.0, "source_answer": 526320.0}
{"source_row": 159, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {772: [(436, 30.0), (721, 30.0)], 436: [(395, 5.0)], 721: [(395, 5.0)], 395: [(114, 1440.0)], 114: [(771, 0.0)], 771: [], 752: [(772, 2880.0)]}\nsource = 752\ntarget = 771\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4355.0, "source_answer": 4355.0}
{"source_row": 160, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {901: [(591, 2.0)], 943: [(591, 2.0)], 591: [(230, 20.0)], 230: [(174, 1.0)], 174: [(825, 0.0)], 825: [], 998: [(901, 5.0), (943, 15.0)]}\nsource = 998\ntarget = 825\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 38.0, "source_answer": 38.0}
{"source_row": 161, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {937: [(143, 15.0)], 110: [(143, 15.0)], 143: [(527, 0.05)], 527: [(471, 180.0)], 471: [(529, 0.0)], 529: [], 102: [(937, 1.0), (110, 1.0)]}\nsource = 102\ntarget = 529\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 196.05, "source_answer": 196.05}
{"source_row": 162, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {108: [(353, 10.0)], 353: [(666, 259200.0)], 666: [(937, 259200.0), (771, 259200.0)], 937: [(500, 0.0)], 771: [(500, 0.0)], 500: [], 451: [(108, 60.0)]}\nsource = 451\ntarget = 500\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 518470.0, "source_answer": 518470.0}
{"source_row": 163, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {886: [(63, 60.0)], 63: [(911, 1440.0)], 911: [(723, 525600.0), (120, 525600.0)], 723: [(233, 0.0)], 120: [(233, 0.0)], 233: [], 91: [(886, 1440.0)]}\nsource = 91\ntarget = 233\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 528540.0, "source_answer": 528540.0}
{"source_row": 164, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(756, 120.0), (245, 1440.0)], 756: [(697, 180.0)], 697: [(423, 2880.0)], 423: [(812, 0.0)], 245: [(697, 180.0)], 812: [], 86: [(863, 2880.0)]}\nsource = 86\ntarget = 812\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7380.0, "source_answer": 7380.0}
{"source_row": 165, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {32: [(773, 5.0)], 683: [(773, 5.0)], 773: [(550, 5.0)], 550: [(231, 4320.0)], 231: [(168, 0.0)], 168: [], 546: [(32, 120.0), (683, 120.0)]}\nsource = 546\ntarget = 168\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4450.0, "source_answer": 4450.0}
{"source_row": 166, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {359: [(418, 1440.0)], 113: [(418, 1440.0)], 418: [(629, 1440.0)], 629: [(899, 1440.0)], 899: [(705, 0.0)], 705: [], 577: [(359, 60.0), (113, 2880.0)]}\nsource = 577\ntarget = 705\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7200.0, "source_answer": 7200.0}
{"source_row": 167, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {317: [(422, 30.0)], 422: [(757, 5.0)], 757: [(802, 900.0)], 802: [(628, 60.0), (924, 180.0)], 924: [(300, 0.0)], 628: [(300, 0.0)], 300: [], 806: [(317, 120.0)]}\nsource = 806\ntarget = 300\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1235.0, "source_answer": 1235.0}
{"source_row": 168, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {285: [(699, 60.0)], 677: [(699, 60.0)], 699: [(296, 30.0)], 296: [(431, 10.0)], 431: [(43, 0.0)], 43: [], 571: [(285, 30.0), (677, 216000.0)]}\nsource = 571\ntarget = 43\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 216100.0, "source_answer": 216100.0}
{"source_row": 169, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {583: [(672, 2.0)], 672: [(605, 30.0), (621, 15.0)], 621: [(300, 5.0)], 605: [(300, 5.0)], 300: [(779, 45.0)], 779: [(164, 0.0)], 164: [], 572: [(583, 1.0)]}\nsource = 572\ntarget = 164\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 83.0, "source_answer": 83.0}
{"source_row": 170, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {910: [(17, 2880.0)], 848: [(17, 2880.0)], 17: [(407, 1440.0)], 407: [(279, 0.0)], 290: [(17, 2880.0)], 279: [], 119: [(910, 4320.0), (848, 2880.0), (290, 4320.0)]}\nsource = 119\ntarget = 279\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8640.0, "source_answer": 8640.0}
{"source_row": 171, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {638: [(256, 0.0)], 392: [(347, 30.0)], 347: [(307, 10.0)], 307: [(506, 15.0)], 506: [(256, 0.0)], 256: [], 255: [(638, 30.0), (392, 60.0)]}\nsource = 255\ntarget = 256\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 115.0, "source_answer": 115.0}
{"source_row": 172, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {223: [(600, 4320.0)], 959: [(600, 4320.0)], 600: [(306, 15.0)], 306: [(391, 10.0)], 391: [(891, 0.0)], 891: [], 652: [(223, 4320.0), (959, 1.0)]}\nsource = 652\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8665.0, "source_answer": 8665.0}
{"source_row": 173, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {163: [(992, 20.0)], 362: [(992, 20.0)], 992: [(516, 1.0)], 516: [(712, 5.0)], 712: [(975, 0.0)], 975: [], 170: [(163, 5.0), (362, 5.0)]}\nsource = 170\ntarget = 975\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0}
{"source_row": 174, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {255: [(605, 43200.0), (994, 43200.0)], 605: [(129, 15.0)], 994: [(129, 15.0)], 129: [(357, 120.0)], 357: [(404, 0.0)], 404: [], 16: [(255, 15.0)]}\nsource = 16\ntarget = 404\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43350.0, "source_answer": 43350.0}
{"source_row": 175, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {65: [(505, 180.0)], 505: [(124, 1440.0), (113, 60.0)], 113: [(14, 30.0)], 124: [(14, 30.0)], 14: [(363, 30.0)], 363: [(553, 0.0)], 553: [], 117: [(65, 4320.0)]}\nsource = 117\ntarget = 553\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6000.0, "source_answer": 6000.0}
{"source_row": 176, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {504: [(819, 60.0)], 814: [(872, 30.0)], 872: [(819, 60.0), (188, 60.0)], 819: [(624, 43200.0)], 188: [(624, 43200.0)], 624: [(491, 0.0)], 491: [], 787: [(504, 60.0), (814, 120.0)]}\nsource = 787\ntarget = 491\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43410.0, "source_answer": 43410.0}
{"source_row": 177, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {635: [(114, 0.08), (884, 0.33)], 114: [(732, 0.5)], 884: [(732, 0.5)], 732: [(630, 2.0)], 630: [(547, 10.0)], 547: [(301, 10.0)], 301: [(186, 0.0)], 186: [], 95: [(635, 0.08)]}\nsource = 95\ntarget = 186\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.91, "source_answer": 22.91}
{"source_row": 178, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {714: [(980, 2.0)], 220: [(973, 5.0)], 973: [(173, 2880.0)], 173: [(636, 43200.0)], 636: [(517, 0.0)], 980: [(636, 43200.0)], 517: [], 293: [(714, 10.0), (220, 60.0)]}\nsource = 293\ntarget = 517\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46145.0, "source_answer": 46145.0}
{"source_row": 179, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {575: [(445, 0.08)], 445: [(847, 0.0)], 726: [(654, 0.05)], 654: [(645, 0.05)], 645: [(445, 0.08)], 847: [], 835: [(575, 0.17), (726, 0.03)]}\nsource = 835\ntarget = 847\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.25, "source_answer": 0.25}
{"source_row": 180, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {743: [(814, 180.0)], 847: [(814, 180.0)], 814: [(144, 5.0)], 144: [(568, 120.0)], 568: [(454, 60.0)], 454: [(915, 0.0)], 915: [], 809: [(743, 180.0), (847, 60.0)]}\nsource = 809\ntarget = 915\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 545.0, "source_answer": 545.0}
{"source_row": 181, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {757: [(787, 5.0)], 682: [(787, 5.0)], 787: [(383, 30.0)], 383: [(369, 5.0)], 369: [(519, 10.0)], 519: [(116, 0.0)], 116: [], 79: [(757, 20.0), (682, 5.0)]}\nsource = 79\ntarget = 116\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 182, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {868: [(68, 15.0), (599, 15.0)], 68: [(67, 60.0)], 599: [(67, 60.0)], 67: [(956, 15.0)], 956: [(254, 15.0)], 254: [(943, 0.0)], 943: [], 664: [(868, 15.0)]}\nsource = 664\ntarget = 943\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 183, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {755: [(200, 30.0)], 104: [(200, 30.0)], 200: [(630, 10.0)], 630: [(656, 0.0)], 830: [(200, 30.0)], 656: [], 101: [(755, 20.0), (104, 10.0), (830, 20.0)]}\nsource = 101\ntarget = 656\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 184, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {906: [(955, 0.5)], 339: [(851, 0.5)], 851: [(871, 0.5)], 871: [(955, 0.5)], 955: [(690, 10.0)], 690: [(555, 0.5)], 555: [(804, 0.0)], 804: [], 172: [(906, 10.0), (339, 10.0)]}\nsource = 172\ntarget = 804\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 185, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {519: [(161, 2.0)], 225: [(161, 2.0)], 161: [(814, 2.0)], 814: [(293, 0.0)], 27: [(161, 2.0)], 293: [], 783: [(519, 2.0), (225, 2.0), (27, 2.0)]}\nsource = 783\ntarget = 293\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 186, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {184: [(684, 15.0)], 684: [(620, 15.0)], 620: [(403, 15.0)], 403: [(60, 120.0)], 60: [(394, 0.0)], 633: [(684, 15.0)], 394: [], 941: [(184, 60.0), (633, 15.0)]}\nsource = 941\ntarget = 394\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 225.0, "source_answer": 225.0}
{"source_row": 187, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {16: [(301, 0.0)], 952: [(301, 0.0)], 229: [(301, 0.0)], 403: [(291, 5.0)], 291: [(301, 0.0)], 301: [], 885: [(16, 86400.0), (952, 10080.0), (229, 10080.0), (403, 60.0)]}\nsource = 885\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86400.0, "source_answer": 86400.0}
{"source_row": 188, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {34: [(675, 1440.0)], 675: [(682, 20160.0)], 682: [(703, 1440.0), (627, 1440.0)], 703: [(475, 0.0)], 627: [(475, 0.0)], 475: [], 903: [(34, 1440.0)]}\nsource = 903\ntarget = 475\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24480.0, "source_answer": 24480.0}
{"source_row": 189, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {205: [(170, 1.0)], 170: [(479, 15.0)], 479: [(319, 5.0), (509, 5.0)], 319: [(851, 5.0)], 509: [(851, 5.0)], 851: [(4, 1.0)], 4: [(585, 0.0)], 585: [], 971: [(205, 1.0)]}\nsource = 971\ntarget = 585\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0}
{"source_row": 190, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {506: [(546, 1.0)], 546: [(45, 1.0)], 45: [(618, 7.0), (91, 5.0)], 618: [(238, 0.0)], 91: [(238, 0.0)], 238: [], 553: [(506, 1.0)]}\nsource = 553\ntarget = 238\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 191, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {103: [(113, 3.0)], 113: [(373, 3.0), (995, 2.0)], 995: [(52, 2.0)], 373: [(52, 2.0)], 52: [(339, 5.0)], 339: [(181, 0.0)], 181: [], 541: [(103, 5.0)]}\nsource = 541\ntarget = 181\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 192, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {542: [(999, 0.17)], 732: [(999, 0.17)], 999: [(377, 0.17)], 377: [(245, 0.17)], 245: [(427, 0.17)], 427: [(505, 0.0)], 505: [], 356: [(542, 0.17), (732, 0.17)]}\nsource = 356\ntarget = 505\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.85, "source_answer": 0.85}
{"source_row": 193, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {875: [(521, 2.0)], 521: [(370, 2.0)], 370: [(593, 3.0)], 593: [(917, 5.0), (359, 3.0)], 359: [(646, 0.0)], 917: [(646, 0.0)], 646: [], 144: [(875, 2.0)]}\nsource = 144\ntarget = 646\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.0, "source_answer": 14.0}
{"source_row": 194, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {281: [(815, 0.58)], 855: [(815, 0.58)], 815: [(882, 0.33), (762, 0.33)], 882: [(574, 0.0)], 762: [(574, 0.0)], 574: [], 119: [(281, 0.42), (855, 0.42)]}\nsource = 119\ntarget = 574\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.33, "source_answer": 1.33}
{"source_row": 195, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {557: [(863, 2.0)], 863: [(139, 3.0)], 139: [(417, 6.0)], 417: [(982, 3.0), (499, 3.0)], 499: [(0, 0.0)], 982: [(0, 0.0)], 0: [], 341: [(557, 4.0)]}\nsource = 341\ntarget = 0\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 196, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(384, 1.0)], 384: [(996, 1.0)], 996: [(422, 1.0)], 422: [(478, 1.0)], 505: [(384, 1.0)], 478: [(812, 1.0)], 812: [(883, 0.0)], 883: [], 431: [(863, 2.0), (505, 2.0)]}\nsource = 431\ntarget = 883\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 197, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {444: [(886, 1.0)], 886: [(862, 1.0)], 862: [(519, 1.0)], 519: [(580, 0.0)], 848: [(886, 1.0)], 580: [], 885: [(444, 1.0), (848, 1.0)]}\nsource = 885\ntarget = 580\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 198, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {497: [(142, 2.0), (878, 2.0), (558, 2.0)], 142: [(313, 1.0)], 558: [(313, 1.0)], 878: [(313, 1.0)], 313: [(634, 1.0)], 634: [(66, 10.0)], 66: [(564, 0.0)], 564: [], 167: [(497, 12.0)]}\nsource = 167\ntarget = 564\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 26.0, "source_answer": 26.0}
{"source_row": 199, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {711: [(237, 0.25), (348, 0.08), (122, 0.17)], 237: [(92, 0.17)], 122: [(117, 0.25)], 348: [(573, 0.0)], 92: [(573, 0.0)], 117: [(573, 0.0)], 573: [], 812: [(711, 4.0)]}\nsource = 812\ntarget = 573\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.42, "source_answer": 4.42}
{"source_row": 200, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {157: [(269, 2.0)], 269: [(57, 5.0), (353, 1.0)], 353: [(340, 20.0)], 57: [(963, 0.0)], 340: [(963, 0.0)], 963: [], 393: [(157, 1.0)]}\nsource = 393\ntarget = 963\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24.0, "source_answer": 24.0}
{"source_row": 201, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {705: [(950, 0.0)], 81: [(161, 86400.0)], 161: [(950, 0.0)], 102: [(950, 0.0)], 378: [(102, 86400.0)], 950: [], 423: [(705, 1.0), (81, 86400.0), (378, 20.0)]}\nsource = 423\ntarget = 950\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 172800.0, "source_answer": 172800.0}
{"source_row": 202, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {682: [(53, 2.0)], 53: [(217, 1.0), (429, 1.0)], 429: [(971, 1.0)], 217: [(971, 1.0)], 971: [(28, 0.0)], 916: [(53, 2.0)], 28: [], 784: [(682, 0.25), (916, 0.33)]}\nsource = 784\ntarget = 28\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.33, "source_answer": 4.33}
{"source_row": 203, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {869: [(73, 60.0)], 73: [(875, 5.0), (523, 60.0)], 523: [(116, 20.0)], 875: [(116, 20.0)], 116: [(977, 0.0)], 977: [], 310: [(869, 60.0)]}\nsource = 310\ntarget = 977\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 200.0, "source_answer": 200.0}
{"source_row": 204, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(902, 0.05)], 141: [(902, 0.05)], 902: [(597, 0.02)], 597: [(507, 1.0)], 507: [(153, 0.0)], 153: [], 129: [(963, 0.17), (141, 0.33)]}\nsource = 129\ntarget = 153\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.4, "source_answer": 1.4}
{"source_row": 205, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {563: [(411, 0.75)], 411: [(295, 30.0), (545, 30.0)], 545: [(562, 0.5)], 295: [(562, 0.5)], 562: [(75, 0.0)], 75: [], 898: [(563, 5.0)]}\nsource = 898\ntarget = 75\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.25, "source_answer": 36.25}
{"source_row": 206, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {457: [(110, 0.42)], 849: [(110, 0.42)], 110: [(469, 0.58)], 469: [(426, 0.5)], 426: [(643, 0.0)], 643: [], 510: [(457, 0.5), (849, 0.42)]}\nsource = 510\ntarget = 643\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.0, "source_answer": 2.0}
{"source_row": 207, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {982: [(433, 0.17)], 433: [(839, 0.17), (309, 0.17)], 309: [(634, 0.17)], 839: [(634, 0.17)], 634: [(912, 0.0)], 912: [], 954: [(982, 0.17)]}\nsource = 954\ntarget = 912\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.68, "source_answer": 0.68}
{"source_row": 208, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {170: [(783, 1.0)], 783: [(671, 2.0)], 671: [(752, 2.0)], 752: [(271, 5.0), (572, 35.0)], 572: [(28, 0.0)], 271: [(28, 0.0)], 28: [], 808: [(170, 1.0)]}\nsource = 808\ntarget = 28\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 209, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {820: [(137, 0.5)], 428: [(137, 0.5)], 137: [(479, 0.5)], 479: [(776, 0.5)], 776: [(777, 2.0)], 777: [(869, 0.0)], 869: [], 439: [(820, 1.0), (428, 1.0)]}\nsource = 439\ntarget = 869\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.5, "source_answer": 4.5}
{"source_row": 210, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {725: [(45, 0.5)], 45: [(524, 1.0)], 524: [(429, 2.0), (927, 10.0)], 429: [(57, 0.0)], 927: [(57, 0.0)], 57: [], 769: [(725, 0.5)]}\nsource = 769\ntarget = 57\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 211, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {112: [(255, 6.0)], 255: [(370, 3.0), (430, 2.0)], 430: [(405, 2.0)], 370: [(405, 2.0)], 405: [(169, 0.0)], 169: [], 685: [(112, 2.0)]}\nsource = 685\ntarget = 169\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 212, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {507: [(109, 15.0)], 109: [(99, 1.0), (314, 1.0)], 314: [(33, 20.0)], 99: [(33, 20.0)], 33: [(502, 0.0)], 502: [], 332: [(507, 15.0)]}\nsource = 332\ntarget = 502\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 51.0, "source_answer": 51.0}
{"source_row": 213, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {533: [(409, 0.42), (327, 0.42)], 409: [(584, 0.58)], 327: [(584, 0.58)], 584: [(910, 0.42)], 910: [(207, 0.0)], 207: [], 164: [(533, 1.0)]}\nsource = 164\ntarget = 207\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.42, "source_answer": 2.42}
{"source_row": 214, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {288: [(387, 0.5)], 328: [(426, 0.42)], 426: [(194, 0.58)], 194: [(608, 0.0)], 255: [(194, 0.58)], 933: [(194, 0.58)], 387: [(608, 0.0)], 608: [], 854: [(288, 0.42), (328, 0.42), (255, 0.33), (933, 0.33)]}\nsource = 854\ntarget = 608\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.42, "source_answer": 1.42}
{"source_row": 215, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {539: [(655, 15.0), (405, 15.0)], 655: [(822, 5.0)], 405: [(23, 20.0)], 822: [(777, 5.0)], 23: [(822, 5.0)], 777: [(808, 15.0)], 808: [(573, 0.0)], 573: [], 889: [(539, 25.0)]}\nsource = 889\ntarget = 573\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 216, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {456: [(920, 3.0)], 920: [(279, 10.0)], 279: [(958, 10.0)], 958: [(399, 0.0)], 100: [(920, 3.0)], 399: [], 948: [(456, 0.5), (100, 0.33)]}\nsource = 948\ntarget = 399\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.5, "source_answer": 23.5}
{"source_row": 217, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {464: [(405, 1.0)], 890: [(405, 1.0)], 405: [(492, 5.0)], 492: [(496, 0.0)], 767: [(405, 1.0)], 496: [], 744: [(464, 1.0), (890, 1.0), (767, 1.0)]}\nsource = 744\ntarget = 496\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 218, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {871: [(584, 0.5)], 14: [(362, 1.0)], 362: [(584, 0.5)], 584: [(911, 0.17)], 911: [(345, 0.17)], 345: [(9, 0.0)], 9: [], 137: [(871, 1.0), (14, 1.0)]}\nsource = 137\ntarget = 9\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.84, "source_answer": 2.84}
{"source_row": 219, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {772: [(862, 0.5)], 862: [(333, 0.58)], 333: [(828, 0.67)], 828: [(452, 0.58), (399, 0.58)], 399: [(454, 0.58)], 452: [(454, 0.58)], 454: [(641, 0.0)], 641: [], 737: [(772, 0.42)]}\nsource = 737\ntarget = 641\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.33, "source_answer": 3.33}
{"source_row": 220, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {195: [(125, 0.5)], 125: [(179, 0.17)], 179: [(189, 1.0), (762, 1.0)], 189: [(932, 0.0)], 762: [(932, 0.0)], 932: [], 430: [(195, 0.5)]}\nsource = 430\ntarget = 932\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.17, "source_answer": 2.17}
{"source_row": 221, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {455: [(781, 6.0), (677, 4.0)], 781: [(645, 4.0)], 677: [(645, 4.0)], 645: [(326, 3.0)], 326: [(393, 0.0)], 393: [], 740: [(455, 4.0)]}\nsource = 740\ntarget = 393\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 222, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {769: [(651, 1.0)], 651: [(367, 1.0), (344, 1.0)], 344: [(786, 1.0)], 367: [(786, 1.0)], 786: [(610, 0.0)], 610: [], 38: [(769, 1.0)]}\nsource = 38\ntarget = 610\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 223, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {644: [(574, 4.0)], 574: [(917, 5.0)], 917: [(680, 5.0)], 680: [(982, 0.0)], 403: [(574, 4.0)], 982: [], 702: [(644, 0.5), (403, 0.5)]}\nsource = 702\ntarget = 982\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.5, "source_answer": 14.5}
{"source_row": 224, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {20: [(843, 0.42)], 822: [(843, 0.42)], 843: [(945, 0.0)], 994: [(843, 0.42)], 903: [(843, 0.42)], 522: [(843, 0.42)], 945: [], 596: [(20, 0.5), (822, 0.42), (994, 0.42), (903, 0.42), (522, 0.42)]}\nsource = 596\ntarget = 945\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.92, "source_answer": 0.92}
{"source_row": 225, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {445: [(675, 1.0)], 675: [(159, 4.0)], 159: [(220, 5.0), (53, 5.0)], 220: [(583, 2.0)], 53: [(583, 2.0)], 583: [(807, 0.0)], 807: [], 628: [(445, 1.0)]}\nsource = 628\ntarget = 807\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 226, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {752: [(681, 15.0)], 428: [(681, 15.0)], 681: [(201, 15.0)], 201: [(896, 0.0)], 72: [(681, 15.0)], 896: [], 441: [(752, 120.0), (428, 120.0), (72, 120.0)]}\nsource = 441\ntarget = 896\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 227, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {735: [(27, 1.0), (421, 1.0)], 27: [(659, 1.0)], 421: [(659, 1.0)], 659: [(310, 0.5)], 310: [(247, 1.0)], 247: [(750, 0.0)], 750: [], 207: [(735, 2.0)]}\nsource = 207\ntarget = 750\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.5, "source_answer": 5.5}
{"source_row": 228, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {719: [(247, 0.17)], 207: [(247, 0.17)], 247: [(159, 0.17)], 159: [(461, 0.17)], 461: [(503, 0.0)], 503: [], 961: [(719, 0.17), (207, 0.17)]}\nsource = 961\ntarget = 503\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.68, "source_answer": 0.68}
{"source_row": 229, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {753: [(930, 1.0)], 443: [(355, 1.0), (534, 1.0)], 534: [(129, 1.0)], 355: [(129, 1.0)], 129: [(792, 0.0)], 930: [(443, 1.0)], 792: [], 269: [(753, 2.0)]}\nsource = 269\ntarget = 792\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 230, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {305: [(779, 0.5)], 334: [(779, 0.5)], 779: [(739, 0.05)], 739: [(69, 0.33)], 69: [(477, 0.0)], 477: [], 39: [(305, 0.03), (334, 0.03)]}\nsource = 39\ntarget = 477\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.91, "source_answer": 0.91}
{"source_row": 231, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {684: [(2, 5.0)], 2: [(414, 15.0), (384, 15.0)], 384: [(966, 45.0)], 414: [(966, 45.0)], 966: [(510, 15.0)], 510: [(645, 5.0)], 645: [(331, 0.0)], 331: [], 5: [(684, 15.0)]}\nsource = 5\ntarget = 331\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0}
{"source_row": 232, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {750: [(173, 5.0)], 173: [(329, 5.0), (55, 5.0)], 55: [(847, 5.0)], 329: [(847, 5.0)], 847: [(720, 0.5)], 720: [(985, 0.5)], 985: [(371, 0.0)], 371: [], 503: [(750, 10.0)]}\nsource = 503\ntarget = 371\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 26.0, "source_answer": 26.0}
{"source_row": 233, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {679: [(835, 45.0), (139, 15.0)], 835: [(839, 15.0)], 139: [(839, 15.0)], 839: [(770, 4.0)], 770: [(665, 0.0)], 665: [], 720: [(679, 15.0)]}\nsource = 720\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 79.0, "source_answer": 79.0}
{"source_row": 234, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {366: [(932, 5.0)], 932: [(924, 3.0)], 924: [(874, 30.0)], 874: [(915, 0.0)], 285: [(932, 5.0)], 915: [], 899: [(366, 30.0), (285, 3.0)]}\nsource = 899\ntarget = 915\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 68.0, "source_answer": 68.0}
{"source_row": 235, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {134: [(906, 60.0), (112, 60.0)], 906: [(758, 1440.0)], 112: [(758, 1440.0)], 758: [(97, 40320.0)], 97: [(435, 0.0)], 435: [], 622: [(134, 120.0)]}\nsource = 622\ntarget = 435\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41940.0, "source_answer": 41940.0}
{"source_row": 236, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {86: [(574, 1.0), (69, 30.0)], 574: [(45, 4320.0)], 69: [(45, 4320.0)], 45: [(588, 20.0)], 588: [(635, 30.0)], 635: [(281, 60.0)], 281: [(368, 0.0)], 368: [], 968: [(86, 2.0)]}\nsource = 968\ntarget = 368\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4462.0, "source_answer": 4462.0}
{"source_row": 237, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {754: [(118, 10.0), (846, 30.0)], 118: [(837, 0.25)], 846: [(376, 2.0)], 837: [(91, 2.0)], 376: [(833, 2.0)], 91: [(376, 2.0)], 833: [(259, 0.0)], 259: [], 363: [(754, 10.0)]}\nsource = 363\ntarget = 259\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 44.0, "source_answer": 44.0}
{"source_row": 238, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {167: [(127, 60.0), (556, 1.0)], 127: [(158, 0.5)], 556: [(158, 0.5)], 158: [(965, 5.0)], 965: [(984, 0.0)], 984: [], 421: [(167, 0.5)]}\nsource = 421\ntarget = 984\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 66.0, "source_answer": 66.0}
{"source_row": 239, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {503: [(451, 0.58)], 451: [(679, 0.57), (187, 2.0)], 187: [(662, 0.0)], 679: [(973, 1.0)], 973: [(900, 1.0)], 900: [(71, 1.0)], 71: [(662, 0.0)], 662: [], 448: [(503, 0.42)]}\nsource = 448\ntarget = 662\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.57, "source_answer": 4.57}
{"source_row": 240, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {18: [(741, 5.0)], 741: [(755, 15.0), (212, 15.0)], 212: [(293, 15.0)], 755: [(293, 15.0)], 293: [(911, 60.0)], 911: [(674, 0.0)], 674: [], 367: [(18, 15.0)]}\nsource = 367\ntarget = 674\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 110.0, "source_answer": 110.0}
{"source_row": 241, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {388: [(548, 3.0)], 548: [(511, 3.0)], 511: [(815, 1.0)], 815: [(188, 150.0), (128, 10.0)], 128: [(935, 0.0)], 188: [(935, 0.0)], 935: [], 729: [(388, 10.0)]}\nsource = 729\ntarget = 935\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 167.0, "source_answer": 167.0}
{"source_row": 242, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {768: [(448, 60.0), (867, 30.0)], 448: [(987, 60.0)], 867: [(987, 60.0)], 987: [(417, 10.0)], 417: [(570, 20.0)], 570: [(719, 5.0)], 719: [(26, 0.0)], 26: [], 107: [(768, 15.0)]}\nsource = 107\ntarget = 26\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 170.0, "source_answer": 170.0}
{"source_row": 243, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {295: [(610, 1440.0)], 572: [(610, 1440.0)], 610: [(377, 1440.0)], 377: [(390, 240.0)], 390: [(321, 0.0)], 321: [], 744: [(295, 1440.0), (572, 1440.0)]}\nsource = 744\ntarget = 321\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4560.0, "source_answer": 4560.0}
{"source_row": 244, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {997: [(901, 5.0)], 998: [(901, 5.0)], 901: [(467, 5.0)], 467: [(180, 20.0)], 180: [(755, 0.0)], 755: [], 658: [(997, 20.0), (998, 20.0)]}\nsource = 658\ntarget = 755\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 245, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {560: [(79, 60.0)], 79: [(590, 1440.0)], 590: [(363, 41760.0)], 87: [(79, 60.0)], 363: [(8, 0.0)], 8: [], 896: [(560, 2.0), (87, 15.0)]}\nsource = 896\ntarget = 8\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43275.0, "source_answer": 43275.0}
{"source_row": 246, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {574: [(528, 1.0)], 528: [(19, 1.0)], 19: [(99, 0.5), (420, 0.5)], 99: [(351, 1.0)], 420: [(351, 1.0)], 351: [(367, 240.0)], 367: [(410, 0.0)], 410: [], 272: [(574, 5.0)]}\nsource = 272\ntarget = 410\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 248.5, "source_answer": 248.5}
{"source_row": 247, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {830: [(261, 0.08), (98, 2.0)], 261: [(128, 0.17)], 98: [(128, 0.17)], 128: [(431, 1.0)], 431: [(273, 0.0)], 273: [], 69: [(830, 5.0)]}\nsource = 69\ntarget = 273\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.17, "source_answer": 8.17}
{"source_row": 248, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {236: [(392, 30.0)], 392: [(721, 30.0), (374, 45.0)], 374: [(66, 0.0)], 721: [(48, 10.0)], 48: [(66, 0.0)], 66: [], 878: [(236, 10080.0)]}\nsource = 878\ntarget = 66\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10155.0, "source_answer": 10155.0}
{"source_row": 249, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {654: [(453, 120.0), (916, 120.0), (218, 2880.0)], 617: [(497, 60.0)], 497: [(270, 5.0)], 270: [(725, 0.0)], 916: [(497, 60.0)], 453: [(497, 60.0)], 218: [(497, 60.0)], 725: [], 390: [(654, 1440.0), (617, 1440.0)]}\nsource = 390\ntarget = 725\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4385.0, "source_answer": 4385.0}
{"source_row": 250, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {103: [(50, 120.0)], 811: [(338, 2880.0)], 338: [(620, 43200.0)], 620: [(954, 0.0)], 50: [(620, 43200.0)], 954: [], 494: [(103, 10.0), (811, 60.0)]}\nsource = 494\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46140.0, "source_answer": 46140.0}
{"source_row": 251, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {502: [(35, 15.0), (679, 5.0)], 35: [(8, 1.0)], 679: [(8, 1.0)], 8: [(620, 4.0)], 620: [(5, 2.0)], 5: [(366, 0.0)], 366: [], 733: [(502, 5.0)]}\nsource = 733\ntarget = 366\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 252, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {185: [(911, 1.0), (258, 0.17)], 911: [(967, 0.5)], 258: [(967, 0.5)], 967: [(843, 0.02)], 843: [(821, 0.0)], 821: [], 547: [(185, 1.0)]}\nsource = 547\ntarget = 821\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.52, "source_answer": 2.52}
{"source_row": 253, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {862: [(686, 86400.0)], 686: [(531, 10080.0), (960, 8640.0)], 960: [(678, 10080.0)], 531: [(678, 10080.0)], 678: [(514, 0.0)], 514: [], 566: [(862, 20160.0)]}\nsource = 566\ntarget = 514\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 126720.0, "source_answer": 126720.0}
{"source_row": 254, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {512: [(194, 2.0)], 194: [(150, 0.58), (239, 5.0), (36, 2.0)], 239: [(610, 2.0)], 150: [(839, 15.0)], 36: [(839, 15.0)], 610: [(685, 0.0)], 839: [(685, 0.0)], 685: [], 964: [(512, 5.0)]}\nsource = 964\ntarget = 685\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24.0, "source_answer": 24.0}
{"source_row": 255, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {816: [(162, 1.0)], 162: [(54, 1.0), (184, 4.0)], 184: [(829, 1.0)], 54: [(609, 0.0)], 829: [(841, 0.5)], 841: [(609, 0.0)], 609: [], 409: [(816, 1.0)]}\nsource = 409\ntarget = 609\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.5, "source_answer": 7.5}
{"source_row": 256, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {567: [(584, 0.75)], 2: [(53, 30.0)], 53: [(657, 5.0)], 657: [(755, 0.25)], 755: [(957, 0.0)], 584: [(755, 0.25)], 957: [], 479: [(567, 1.0), (2, 1.0)]}\nsource = 479\ntarget = 957\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.25, "source_answer": 36.25}
{"source_row": 257, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {763: [(92, 60.0)], 647: [(92, 60.0)], 92: [(729, 0.5), (24, 0.5)], 729: [(439, 0.0)], 24: [(439, 0.0)], 119: [(92, 60.0)], 439: [], 949: [(763, 60.0), (647, 120.0), (119, 60.0)]}\nsource = 949\ntarget = 439\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.5, "source_answer": 180.5}
{"source_row": 258, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {638: [(962, 5.0)], 962: [(842, 15.0), (497, 15.0)], 497: [(244, 15.0)], 842: [(244, 15.0)], 244: [(902, 120.0)], 902: [(676, 0.0)], 676: [], 234: [(638, 15.0)]}\nsource = 234\ntarget = 676\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 170.0, "source_answer": 170.0}
{"source_row": 259, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {749: [(136, 5.0), (964, 5.0), (833, 5.0), (922, 20.0)], 136: [(686, 5.0)], 833: [(686, 5.0)], 964: [(686, 5.0)], 922: [(686, 5.0)], 686: [(129, 10.0)], 129: [(124, 0.0)], 124: [], 726: [(749, 10.0)]}\nsource = 726\ntarget = 124\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 260, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {328: [(585, 1.0)], 385: [(585, 1.0)], 585: [(914, 1.0)], 914: [(29, 1.0)], 29: [(356, 2.0)], 356: [(242, 0.0)], 242: [], 661: [(328, 2.0), (385, 3.0)]}\nsource = 661\ntarget = 242\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 261, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {349: [(137, 0.33)], 537: [(196, 1.0)], 196: [(158, 0.17)], 158: [(597, 5.0)], 597: [(368, 0.25)], 137: [(42, 0.0)], 368: [(42, 0.0)], 42: [], 944: [(349, 0.33), (537, 5.0)]}\nsource = 944\ntarget = 42\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.42, "source_answer": 11.42}
{"source_row": 262, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {52: [(60, 0.5)], 94: [(60, 0.5)], 60: [(15, 0.5)], 15: [(35, 0.5)], 35: [(59, 0.0)], 59: [], 500: [(52, 1.0), (94, 1.0)]}\nsource = 500\ntarget = 59\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.5, "source_answer": 2.5}
{"source_row": 263, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {472: [(311, 1.0)], 311: [(720, 0.33)], 720: [(447, 0.33)], 447: [(270, 0.17)], 270: [(697, 0.33), (511, 0.17)], 697: [(203, 0.0)], 511: [(203, 0.0)], 203: [], 32: [(472, 1.0)]}\nsource = 32\ntarget = 203\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.16, "source_answer": 3.16}
{"source_row": 264, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {414: [(489, 1440.0)], 489: [(687, 43200.0), (300, 11520.0)], 300: [(599, 0.0)], 687: [(624, 60.0)], 624: [(599, 0.0)], 599: [], 567: [(414, 1440.0)]}\nsource = 567\ntarget = 599\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46140.0, "source_answer": 46140.0}
{"source_row": 265, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {525: [(625, 0.25)], 625: [(448, 0.5), (516, 1.0)], 516: [(840, 0.33)], 448: [(840, 0.33)], 840: [(253, 0.0)], 253: [], 218: [(525, 2.0)]}\nsource = 218\ntarget = 253\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.58, "source_answer": 3.58}
{"source_row": 266, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {654: [(411, 10.0)], 242: [(336, 10.0)], 336: [(623, 0.0)], 411: [(697, 5.0)], 697: [(336, 10.0)], 623: [], 235: [(654, 5.0), (242, 5.0)]}\nsource = 235\ntarget = 623\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 267, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {334: [(732, 2.0)], 732: [(615, 2.0), (925, 2.0)], 925: [(360, 2.0)], 615: [(360, 2.0)], 360: [(388, 0.0)], 388: [], 979: [(334, 2.0)]}\nsource = 979\ntarget = 388\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 268, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {399: [(463, 5.0)], 463: [(754, 20.0)], 754: [(724, 30.0), (96, 10.0)], 724: [(884, 45.0)], 96: [(884, 45.0)], 884: [(464, 30.0)], 464: [(903, 0.0)], 903: [], 863: [(399, 10.0)]}\nsource = 863\ntarget = 903\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 140.0, "source_answer": 140.0}
{"source_row": 269, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {224: [(169, 10.0), (402, 10.0)], 169: [(346, 1.0)], 402: [(346, 1.0)], 346: [(645, 20.0)], 645: [(311, 0.33)], 311: [(566, 10.0)], 566: [(606, 0.0)], 606: [], 347: [(224, 10.0)]}\nsource = 347\ntarget = 606\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 51.33, "source_answer": 51.33}
{"source_row": 270, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {707: [(369, 1.0)], 369: [(122, 30.0)], 122: [(549, 60.0)], 549: [(579, 60.0), (243, 30.0)], 243: [(694, 0.0)], 579: [(694, 0.0)], 694: [], 134: [(707, 10.0)]}\nsource = 134\ntarget = 694\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 161.0, "source_answer": 161.0}
{"source_row": 271, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {346: [(539, 2.0)], 539: [(217, 1.0)], 217: [(809, 15.0), (919, 15.0), (456, 15.0), (907, 20.0)], 919: [(607, 0.0)], 456: [(607, 0.0)], 809: [(607, 0.0)], 907: [(607, 0.0)], 607: [], 483: [(346, 30.0)]}\nsource = 483\ntarget = 607\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 53.0, "source_answer": 53.0}
{"source_row": 272, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {564: [(421, 60.0)], 421: [(256, 15.0)], 256: [(533, 180.0)], 533: [(113, 2880.0), (728, 60.0)], 728: [(257, 0.0)], 113: [(411, 10080.0)], 411: [(257, 0.0)], 257: [], 153: [(564, 120.0)]}\nsource = 153\ntarget = 257\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13335.0, "source_answer": 13335.0}
{"source_row": 273, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {717: [(912, 15.0)], 912: [(915, 15.0)], 915: [(484, 45.0), (175, 15.0)], 484: [(515, 0.0)], 175: [(515, 0.0)], 515: [], 219: [(717, 45.0)]}\nsource = 219\ntarget = 515\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 274, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {885: [(815, 0.0)], 688: [(157, 15.0), (668, 30.0), (399, 30.0)], 668: [(55, 10.0)], 157: [(55, 10.0)], 399: [(55, 10.0)], 55: [(820, 120.0)], 820: [(815, 0.0)], 815: [], 589: [(885, 120.0), (688, 15.0)]}\nsource = 589\ntarget = 815\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 175.0, "source_answer": 175.0}
{"source_row": 275, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {321: [(581, 5.0), (930, 5.0)], 581: [(992, 10.0)], 930: [(992, 10.0)], 992: [(351, 30.0)], 351: [(218, 0.0)], 218: [], 178: [(321, 5.0)]}\nsource = 178\ntarget = 218\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 276, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {605: [(802, 4.0)], 939: [(802, 4.0)], 802: [(980, 1.0)], 980: [(117, 2.0)], 117: [(990, 0.0)], 990: [], 993: [(605, 3.0), (939, 3.0)]}\nsource = 993\ntarget = 990\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 277, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {174: [(251, 43200.0)], 251: [(512, 43200.0), (278, 43200.0)], 278: [(797, 43200.0)], 512: [(797, 43200.0)], 797: [(700, 60.0)], 700: [(514, 0.0)], 514: [], 703: [(174, 10080.0)]}\nsource = 703\ntarget = 514\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 139740.0, "source_answer": 139740.0}
{"source_row": 278, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {90: [(4, 1.0)], 4: [(82, 0.25), (800, 5.0)], 800: [(912, 35.0)], 82: [(226, 120.0)], 912: [(226, 120.0)], 226: [(754, 0.0)], 754: [], 737: [(90, 4.0)]}\nsource = 737\ntarget = 754\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 165.0, "source_answer": 165.0}
{"source_row": 279, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {947: [(614, 10512000.0)], 851: [(838, 2102400.0)], 838: [(998, 0.0)], 327: [(998, 0.0)], 614: [(998, 0.0)], 998: [], 915: [(947, 10512000.0), (851, 6307200.0), (327, 1440.0)]}\nsource = 915\ntarget = 998\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21024000.0, "source_answer": 21024000.0}
{"source_row": 280, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {95: [(37, 2.0)], 37: [(433, 0.33), (814, 1.0)], 814: [(911, 0.17)], 433: [(911, 0.17)], 911: [(105, 0.0)], 105: [], 792: [(95, 2.0)]}\nsource = 792\ntarget = 105\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.17, "source_answer": 5.17}
{"source_row": 281, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {249: [(980, 5.0)], 980: [(211, 15.0)], 211: [(125, 35.0), (434, 5.0)], 125: [(49, 0.0)], 434: [(49, 0.0)], 49: [], 544: [(249, 15.0)]}\nsource = 544\ntarget = 49\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 282, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {173: [(262, 2.0)], 262: [(353, 2.0), (113, 4.0)], 113: [(442, 4.0)], 353: [(442, 4.0)], 442: [(527, 0.0)], 527: [], 441: [(173, 1.0)]}\nsource = 441\ntarget = 527\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 283, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {975: [(264, 5.0), (919, 5.0)], 264: [(626, 2.0)], 919: [(626, 2.0)], 626: [(963, 1440.0)], 963: [(625, 5.0)], 625: [(418, 5.0)], 418: [(858, 0.0)], 858: [], 635: [(975, 1.0)]}\nsource = 635\ntarget = 858\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1458.0, "source_answer": 1458.0}
{"source_row": 284, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {37: [(481, 0.17)], 481: [(553, 0.17)], 553: [(350, 0.08), (537, 0.05), (376, 2.0)], 537: [(658, 5.0)], 376: [(658, 5.0)], 350: [(658, 5.0)], 658: [(245, 0.0)], 245: [], 751: [(37, 5.0)]}\nsource = 751\ntarget = 245\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.34, "source_answer": 12.34}
{"source_row": 285, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {264: [(4, 5.0)], 4: [(308, 15.0), (496, 15.0)], 496: [(405, 35.0)], 308: [(405, 35.0)], 405: [(647, 25.0)], 647: [(181, 0.0)], 181: [], 997: [(264, 15.0)]}\nsource = 997\ntarget = 181\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.0, "source_answer": 95.0}
{"source_row": 286, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {573: [(899, 30.0)], 899: [(620, 1.0), (223, 1.0)], 223: [(528, 15.0)], 620: [(33, 1.0)], 528: [(33, 1.0)], 33: [(906, 0.0)], 906: [], 474: [(573, 5.0)]}\nsource = 474\ntarget = 906\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 52.0, "source_answer": 52.0}
{"source_row": 287, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {230: [(23, 0.0)], 92: [(533, 2.0)], 533: [(147, 0.5), (919, 0.5)], 147: [(726, 2.0)], 919: [(726, 2.0)], 726: [(23, 0.0)], 23: [], 813: [(230, 4.0), (92, 1.0)]}\nsource = 813\ntarget = 23\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.5, "source_answer": 5.5}
{"source_row": 288, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {805: [(717, 5.0), (634, 1.0)], 717: [(700, 1.0)], 634: [(700, 1.0)], 700: [(827, 30.0)], 827: [(182, 0.0)], 182: [], 578: [(805, 5.0)]}\nsource = 578\ntarget = 182\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 289, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {994: [(335, 35.0)], 415: [(335, 35.0)], 335: [(533, 0.02)], 533: [(329, 129600.0)], 329: [(809, 0.0)], 809: [], 313: [(994, 180.0), (415, 120.0)]}\nsource = 313\ntarget = 809\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129815.02, "source_answer": 129815.02}
{"source_row": 290, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {795: [(316, 30.0)], 316: [(487, 30.0)], 487: [(160, 30.0)], 929: [(894, 0.0)], 160: [(403, 15.0), (929, 60.0)], 403: [(894, 0.0)], 894: [], 395: [(795, 60.0)]}\nsource = 395\ntarget = 894\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 210.0, "source_answer": 210.0}
{"source_row": 291, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {170: [(486, 0.02), (956, 0.02)], 486: [(766, 0.02)], 956: [(766, 0.02)], 766: [(81, 0.08)], 81: [(703, 2.0)], 703: [(690, 0.0)], 690: [], 678: [(170, 1.0)]}\nsource = 678\ntarget = 690\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.12, "source_answer": 3.12}
{"source_row": 292, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {748: [(939, 5.0)], 292: [(880, 20.0)], 880: [(421, 0.0)], 939: [(245, 0.5)], 245: [(880, 20.0)], 421: [], 795: [(748, 1.0), (292, 30.0)]}\nsource = 795\ntarget = 421\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 293, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {272: [(328, 5.0)], 328: [(561, 3.0)], 561: [(314, 3.0)], 314: [(336, 0.5), (846, 0.5)], 846: [(439, 240.0)], 336: [(439, 240.0)], 439: [(236, 0.0)], 236: [], 763: [(272, 15.0)]}\nsource = 763\ntarget = 236\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 266.5, "source_answer": 266.5}
{"source_row": 294, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {202: [(79, 259200.0), (933, 60.0)], 79: [(823, 259200.0)], 933: [(823, 259200.0)], 823: [(86, 1051200.0)], 86: [(704, 0.0)], 704: [], 42: [(202, 120.0)]}\nsource = 42\ntarget = 704\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1569720.0, "source_answer": 1569720.0}
{"source_row": 295, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {738: [(759, 5.0)], 109: [(316, 1.0)], 316: [(788, 20.0)], 788: [(413, 0.0)], 759: [(788, 20.0)], 413: [], 76: [(738, 5.0), (109, 1.0)]}\nsource = 76\ntarget = 413\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 296, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {664: [(349, 0.33), (861, 0.17)], 861: [(694, 0.08)], 349: [(827, 0.17)], 694: [(190, 0.0)], 827: [(190, 0.0)], 190: [], 332: [(664, 0.08)]}\nsource = 332\ntarget = 190\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.58, "source_answer": 0.58}
{"source_row": 297, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {775: [(160, 10.0), (283, 10.0)], 160: [(129, 2.0)], 283: [(129, 2.0)], 129: [(492, 2.0)], 492: [(893, 20.0)], 893: [(469, 86400.0)], 469: [(843, 0.0)], 843: [], 360: [(775, 20.0)]}\nsource = 360\ntarget = 843\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86454.0, "source_answer": 86454.0}
{"source_row": 298, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {75: [(470, 0.17)], 103: [(606, 0.5)], 606: [(759, 10.0)], 759: [(792, 2.0)], 792: [(988, 0.0)], 470: [(897, 1.0)], 897: [(759, 10.0)], 988: [], 235: [(75, 0.25), (103, 2.0)]}\nsource = 235\ntarget = 988\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.5, "source_answer": 14.5}
{"source_row": 299, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {937: [(439, 120.0)], 401: [(435, 15.0)], 435: [(3, 30.0)], 3: [(439, 120.0)], 439: [(324, 20.0)], 324: [(730, 300.0)], 730: [(102, 0.0)], 102: [], 155: [(937, 129600.0), (401, 1440.0)]}\nsource = 155\ntarget = 102\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130040.0, "source_answer": 130040.0}
{"source_row": 300, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {793: [(355, 5.0)], 355: [(409, 2.0), (578, 1.0)], 578: [(548, 1.0)], 409: [(548, 1.0)], 548: [(871, 180.0), (500, 120.0)], 871: [(282, 0.0)], 500: [(282, 0.0)], 282: [], 446: [(793, 20.0)]}\nsource = 446\ntarget = 282\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 208.0, "source_answer": 208.0}
{"source_row": 301, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {510: [(281, 30.0)], 281: [(135, 15.0)], 135: [(296, 30.0)], 296: [(664, 0.0)], 81: [(281, 30.0)], 664: [], 562: [(510, 30.0), (81, 15.0)]}\nsource = 562\ntarget = 664\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 302, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {444: [(857, 2.0)], 857: [(972, 1.0), (305, 5.0)], 305: [(21, 5.0)], 972: [(21, 5.0)], 21: [(92, 0.0)], 92: [], 730: [(444, 1.0)]}\nsource = 730\ntarget = 92\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 303, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {543: [(477, 1.0)], 347: [(648, 1.0)], 648: [(3, 2.0)], 3: [(477, 1.0)], 477: [(876, 0.0)], 876: [], 600: [(543, 0.58), (347, 0.58)]}\nsource = 600\ntarget = 876\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.58, "source_answer": 4.58}
{"source_row": 304, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(323, 1.0)], 452: [(323, 1.0)], 323: [(634, 1.0)], 634: [(639, 1.0)], 639: [(998, 0.0)], 998: [], 475: [(458, 0.5), (452, 0.5)]}\nsource = 475\ntarget = 998\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.5, "source_answer": 3.5}
{"source_row": 305, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {330: [(123, 0.33)], 444: [(479, 0.5)], 479: [(123, 0.33)], 123: [(549, 480.0)], 549: [(50, 0.17)], 50: [(841, 0.0)], 841: [], 112: [(330, 0.58), (444, 1.0)]}\nsource = 112\ntarget = 841\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 482.0, "source_answer": 482.0}
{"source_row": 306, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {591: [(879, 0.17), (85, 0.08), (861, 10.0)], 879: [(285, 10.0)], 861: [(285, 10.0)], 85: [(285, 10.0)], 285: [(166, 2.0)], 166: [(874, 2.0)], 874: [(698, 0.0)], 698: [], 281: [(591, 0.17)]}\nsource = 281\ntarget = 698\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24.17, "source_answer": 24.17}
{"source_row": 307, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {744: [(261, 30.0), (90, 1.0)], 261: [(8, 10.0)], 8: [(952, 60.0)], 952: [(532, 10.0)], 532: [(225, 1.0)], 90: [(952, 60.0)], 225: [(194, 0.0)], 194: [], 837: [(744, 2.0)]}\nsource = 837\ntarget = 194\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 113.0, "source_answer": 113.0}
{"source_row": 308, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {495: [(842, 1.0)], 132: [(842, 1.0)], 842: [(440, 1.0)], 440: [(661, 5.0)], 661: [(485, 0.0)], 485: [], 234: [(495, 1.0), (132, 1.0)]}\nsource = 234\ntarget = 485\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 309, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {532: [(451, 3.0)], 420: [(451, 3.0)], 451: [(244, 2.0)], 244: [(82, 1.0)], 82: [(77, 0.0)], 77: [], 505: [(532, 5.0), (420, 10.0)]}\nsource = 505\ntarget = 77\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 310, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {423: [(977, 1.0)], 977: [(999, 1.0), (371, 1.0)], 371: [(889, 1.0)], 999: [(889, 1.0)], 889: [(335, 0.0)], 335: [], 780: [(423, 1.0)]}\nsource = 780\ntarget = 335\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 311, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {141: [(449, 0.08)], 449: [(78, 0.08)], 78: [(820, 0.08), (32, 0.08)], 820: [(345, 0.0)], 32: [(345, 0.0)], 345: [], 725: [(141, 0.08)]}\nsource = 725\ntarget = 345\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.32, "source_answer": 0.32}
{"source_row": 312, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {695: [(483, 10.0)], 483: [(453, 30.0), (769, 30.0), (235, 15.0)], 769: [(639, 60.0)], 453: [(639, 60.0)], 235: [(639, 60.0)], 639: [(305, 0.0)], 305: [], 386: [(695, 388800.0)]}\nsource = 386\ntarget = 305\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 388900.0, "source_answer": 388900.0}
{"source_row": 313, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {944: [(480, 0.5)], 772: [(480, 0.5)], 480: [(828, 0.17)], 828: [(410, 0.33)], 410: [(692, 0.17)], 692: [(1, 0.0)], 1: [], 540: [(944, 1.0), (772, 1.0)]}\nsource = 540\ntarget = 1\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.17, "source_answer": 2.17}
{"source_row": 314, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {884: [(589, 0.08)], 232: [(935, 10.0)], 935: [(833, 0.0)], 589: [(772, 0.08)], 772: [(935, 10.0)], 833: [], 282: [(884, 0.08), (232, 1.0)]}\nsource = 282\ntarget = 833\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 315, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {843: [(623, 0.17)], 623: [(437, 0.03), (485, 0.02), (915, 0.03)], 485: [(379, 0.08)], 437: [(379, 0.08)], 915: [(379, 0.08)], 379: [(310, 0.0)], 310: [], 137: [(843, 0.17)]}\nsource = 137\ntarget = 310\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.45, "source_answer": 0.45}
{"source_row": 316, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {460: [(839, 0.17)], 189: [(839, 0.17)], 839: [(565, 0.08)], 565: [(850, 0.17)], 850: [(683, 0.05)], 683: [(420, 0.0)], 420: [], 502: [(460, 0.33), (189, 0.08)]}\nsource = 502\ntarget = 420\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.8, "source_answer": 0.8}
{"source_row": 317, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {771: [(490, 10.0)], 719: [(490, 10.0)], 490: [(386, 2.0)], 386: [(778, 5.0)], 778: [(767, 0.0)], 767: [], 469: [(771, 5.0), (719, 2.0)]}\nsource = 469\ntarget = 767\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 318, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {411: [(16, 60.0)], 453: [(16, 60.0)], 16: [(620, 0.02)], 620: [(651, 0.02)], 651: [(794, 0.0)], 794: [], 795: [(411, 1.0), (453, 60.0)]}\nsource = 795\ntarget = 794\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.04, "source_answer": 120.04}
{"source_row": 319, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {521: [(99, 0.05)], 99: [(844, 0.05)], 844: [(227, 3.0), (556, 0.33)], 227: [(525, 0.0)], 556: [(525, 0.0)], 525: [], 926: [(521, 0.17)]}\nsource = 926\ntarget = 525\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.27, "source_answer": 3.27}
{"source_row": 320, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {476: [(757, 60.0)], 554: [(757, 60.0)], 757: [(175, 0.0)], 844: [(757, 60.0)], 341: [(757, 60.0)], 175: [], 641: [(476, 60.0), (554, 60.0), (844, 60.0), (341, 60.0)]}\nsource = 641\ntarget = 175\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 321, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {999: [(392, 15.0), (539, 10.0)], 392: [(318, 2.0)], 539: [(318, 2.0)], 318: [(730, 10.0)], 730: [(551, 5.0)], 551: [(798, 2.0)], 798: [(248, 0.0)], 248: [], 529: [(999, 15.0)]}\nsource = 529\ntarget = 248\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 49.0, "source_answer": 49.0}
{"source_row": 322, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {24: [(904, 1.0)], 904: [(291, 10.0)], 291: [(261, 5.0), (197, 10.0)], 261: [(50, 5.0)], 197: [(50, 5.0)], 50: [(718, 120.0)], 718: [(182, 0.0)], 182: [], 69: [(24, 5.0)]}\nsource = 69\ntarget = 182\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 151.0, "source_answer": 151.0}
{"source_row": 323, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {694: [(136, 2880.0)], 136: [(406, 28800.0), (256, 28800.0)], 256: [(268, 7200.0)], 406: [(871, 4320.0)], 268: [(773, 4320.0)], 871: [(639, 0.0)], 773: [(639, 0.0)], 639: [], 171: [(694, 43200.0)]}\nsource = 171\ntarget = 639\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86400.0, "source_answer": 86400.0}
{"source_row": 324, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {802: [(332, 10080.0), (608, 5.0), (109, 60.0)], 417: [(743, 480.0)], 743: [(497, 0.0)], 332: [(417, 300.0)], 109: [(417, 300.0)], 608: [(417, 300.0)], 497: [], 442: [(802, 20160.0)]}\nsource = 442\ntarget = 497\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31020.0, "source_answer": 31020.0}
{"source_row": 325, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {89: [(413, 10.0)], 413: [(668, 1.0)], 668: [(116, 20.0), (58, 5.0)], 116: [(762, 0.0)], 58: [(762, 0.0)], 762: [], 241: [(89, 10.0)]}\nsource = 241\ntarget = 762\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 326, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {161: [(581, 30.0)], 581: [(789, 5.0)], 789: [(268, 5.0)], 217: [(581, 30.0)], 268: [(766, 30.0)], 766: [(264, 120.0)], 264: [(383, 0.0)], 383: [], 335: [(161, 10.0), (217, 5.0)]}\nsource = 335\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 200.0, "source_answer": 200.0}
{"source_row": 327, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {913: [(395, 0.5)], 395: [(864, 1.0)], 864: [(340, 30.0)], 977: [(239, 0.5)], 239: [(864, 1.0)], 340: [(184, 0.0)], 184: [], 40: [(913, 0.5), (977, 0.5)]}\nsource = 40\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 328, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {519: [(485, 1.0)], 485: [(381, 1.0)], 381: [(326, 5.0)], 326: [(666, 0.0)], 944: [(485, 1.0)], 666: [], 387: [(519, 1.0), (944, 5.0)]}\nsource = 387\ntarget = 666\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 329, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {85: [(538, 1.0)], 491: [(466, 1.0)], 466: [(555, 0.0)], 538: [(870, 0.58), (466, 1.0)], 870: [(555, 0.0)], 555: [], 975: [(85, 2.0), (491, 0.58)]}\nsource = 975\ntarget = 555\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 330, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {656: [(922, 3.0)], 260: [(105, 10.0), (744, 15.0), (551, 10.0)], 744: [(414, 0.0)], 105: [(414, 0.0)], 551: [(414, 0.0)], 565: [(922, 3.0)], 922: [(260, 5.0)], 414: [], 670: [(656, 3.0), (565, 3.0)]}\nsource = 670\ntarget = 414\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 26.0, "source_answer": 26.0}
{"source_row": 331, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {779: [(33, 60.0)], 33: [(588, 60.0)], 588: [(225, 60.0)], 225: [(275, 30.0), (529, 30.0)], 529: [(962, 0.0)], 275: [(962, 0.0)], 962: [], 157: [(779, 60.0)]}\nsource = 157\ntarget = 962\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 270.0, "source_answer": 270.0}
{"source_row": 332, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {93: [(213, 30.0)], 213: [(410, 10080.0)], 410: [(460, 120.0), (745, 43200.0)], 460: [(256, 0.0)], 745: [(256, 0.0)], 256: [], 223: [(93, 10.0)]}\nsource = 223\ntarget = 256\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 53320.0, "source_answer": 53320.0}
{"source_row": 333, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {110: [(141, 60.0)], 674: [(141, 60.0)], 141: [(13, 30.0)], 13: [(896, 60.0)], 896: [(339, 0.0)], 339: [], 689: [(110, 60.0), (674, 60.0)]}\nsource = 689\ntarget = 339\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 210.0, "source_answer": 210.0}
{"source_row": 334, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {612: [(452, 10.0)], 105: [(452, 10.0)], 452: [(950, 5.0)], 950: [(666, 10.0)], 666: [(492, 0.0)], 492: [], 728: [(612, 20.0), (105, 10.0)]}\nsource = 728\ntarget = 492\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 335, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {145: [(575, 5.0)], 93: [(575, 5.0)], 575: [(220, 5.0)], 220: [(32, 5.0)], 32: [(678, 10.0)], 678: [(828, 1.0)], 828: [(629, 0.0)], 629: [], 270: [(145, 2.0), (93, 2.0)]}\nsource = 270\ntarget = 629\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0}
{"source_row": 336, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {884: [(36, 0.33), (490, 0.5)], 36: [(597, 0.08)], 490: [(597, 0.08)], 597: [(102, 0.08)], 102: [(174, 0.0)], 174: [], 70: [(884, 0.17)]}\nsource = 70\ntarget = 174\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.83, "source_answer": 0.83}
{"source_row": 337, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {982: [(52, 1.0)], 266: [(778, 1.0)], 778: [(431, 1.0)], 431: [(52, 1.0)], 52: [(679, 0.0)], 679: [], 591: [(982, 1.0), (266, 1.0)]}\nsource = 591\ntarget = 679\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 338, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {654: [(701, 1.0)], 701: [(652, 1.0)], 652: [(957, 2.0)], 957: [(171, 10.0), (675, 1.0)], 675: [(389, 0.0)], 171: [(389, 0.0)], 389: [], 561: [(654, 2.0)]}\nsource = 561\ntarget = 389\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 339, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {4: [(183, 0.5)], 183: [(86, 15.0)], 86: [(519, 5.0)], 920: [(183, 0.5)], 519: [(561, 1.0)], 561: [(474, 0.0)], 474: [], 444: [(4, 0.5), (920, 1.0)]}\nsource = 444\ntarget = 474\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.5, "source_answer": 22.5}
{"source_row": 340, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {325: [(218, 10.0)], 452: [(424, 10.0)], 424: [(541, 20.0)], 541: [(218, 10.0)], 218: [(816, 10.0)], 816: [(261, 0.0)], 261: [], 365: [(325, 15.0), (452, 20.0)]}\nsource = 365\ntarget = 261\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 341, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {127: [(389, 3.0)], 389: [(808, 1.0)], 808: [(369, 1.0), (168, 0.5)], 369: [(596, 0.5)], 168: [(596, 0.5)], 596: [(142, 0.17)], 142: [(445, 0.0)], 445: [], 149: [(127, 2.0)]}\nsource = 149\ntarget = 445\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.67, "source_answer": 7.67}
{"source_row": 342, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {855: [(519, 5.0), (254, 5.0)], 519: [(918, 1.0)], 254: [(918, 1.0)], 918: [(437, 15.0)], 437: [(530, 0.0)], 530: [], 405: [(855, 5.0)]}\nsource = 405\ntarget = 530\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 26.0, "source_answer": 26.0}
{"source_row": 343, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {13: [(630, 20.0)], 630: [(510, 1.0)], 510: [(423, 0.33), (925, 1.0)], 423: [(181, 30.0)], 925: [(181, 30.0)], 181: [(781, 0.0)], 781: [], 272: [(13, 2.0)]}\nsource = 272\ntarget = 781\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 54.0, "source_answer": 54.0}
{"source_row": 344, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {454: [(405, 1.0)], 405: [(210, 20.0)], 210: [(824, 60.0)], 824: [(127, 0.0)], 854: [(405, 1.0)], 127: [], 253: [(454, 5.0), (854, 2.0)]}\nsource = 253\ntarget = 127\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86.0, "source_answer": 86.0}
{"source_row": 345, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {929: [(655, 60.0)], 142: [(891, 10080.0)], 891: [(655, 60.0)], 655: [(638, 60.0)], 638: [(911, 60.0)], 911: [(854, 0.0)], 854: [], 262: [(929, 60.0), (142, 30.0)]}\nsource = 262\ntarget = 854\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10290.0, "source_answer": 10290.0}
{"source_row": 346, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {576: [(240, 0.25)], 482: [(240, 0.25)], 240: [(45, 0.25)], 45: [(765, 15.0)], 765: [(593, 5.0)], 593: [(90, 0.0)], 90: [], 740: [(576, 0.25), (482, 0.25)]}\nsource = 740\ntarget = 90\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.75, "source_answer": 20.75}
{"source_row": 347, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {219: [(992, 1.0)], 909: [(352, 2.0)], 352: [(992, 1.0)], 992: [(522, 2.0)], 522: [(832, 20.0)], 832: [(794, 0.0)], 794: [], 862: [(219, 20.0), (909, 10.0)]}\nsource = 862\ntarget = 794\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43.0, "source_answer": 43.0}
{"source_row": 348, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {555: [(214, 5.0), (548, 5.0)], 214: [(149, 1.0)], 548: [(149, 1.0)], 149: [(509, 1.0)], 509: [(835, 0.0)], 835: [], 602: [(555, 5.0)]}\nsource = 602\ntarget = 835\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 349, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {595: [(19, 4320.0)], 709: [(635, 4.0)], 635: [(230, 15.0)], 230: [(94, 1.0)], 94: [(781, 5.0)], 19: [(230, 15.0)], 781: [(28, 0.0)], 28: [], 952: [(595, 5.0), (709, 3.0)]}\nsource = 952\ntarget = 28\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4346.0, "source_answer": 4346.0}
{"source_row": 350, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {297: [(769, 2.0)], 769: [(617, 2.0)], 617: [(958, 5.0)], 958: [(607, 10.0), (318, 10.0)], 318: [(315, 2.0)], 607: [(315, 2.0)], 315: [(195, 0.0)], 195: [], 999: [(297, 0.5)]}\nsource = 999\ntarget = 195\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.5, "source_answer": 21.5}
{"source_row": 351, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(57, 1.0)], 57: [(426, 5.0), (827, 20.0)], 827: [(809, 34.0)], 426: [(809, 34.0)], 809: [(87, 0.0)], 87: [], 190: [(963, 1.0)]}\nsource = 190\ntarget = 87\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 56.0, "source_answer": 56.0}
{"source_row": 352, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {330: [(761, 5.0), (209, 10.0)], 761: [(796, 5.0)], 209: [(796, 5.0)], 796: [(485, 30.0)], 485: [(554, 0.0)], 554: [], 738: [(330, 30.0)]}\nsource = 738\ntarget = 554\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 353, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {733: [(715, 20.0)], 715: [(336, 10.0), (889, 10.0)], 889: [(816, 5.0)], 336: [(816, 5.0)], 816: [(261, 0.0)], 261: [], 701: [(733, 10.0)]}\nsource = 701\ntarget = 261\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 354, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {247: [(963, 0.58)], 963: [(362, 0.58)], 362: [(838, 0.58)], 838: [(790, 0.58), (972, 0.42)], 972: [(826, 1.0)], 790: [(826, 1.0)], 826: [(882, 0.0)], 882: [], 183: [(247, 0.58)]}\nsource = 183\ntarget = 882\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.9, "source_answer": 3.9}
{"source_row": 355, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {238: [(685, 1.0), (365, 1.0)], 379: [(210, 1.0)], 210: [(685, 1.0)], 685: [(311, 0.0)], 699: [(210, 1.0)], 365: [(311, 0.0)], 311: [], 684: [(238, 1.0), (379, 1.0), (699, 1.0)]}\nsource = 684\ntarget = 311\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 356, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {214: [(22, 5.0)], 838: [(226, 0.0)], 751: [(467, 30.0)], 467: [(693, 5.0)], 693: [(838, 10.0)], 273: [(22, 5.0)], 22: [(751, 5.0)], 226: [], 85: [(214, 10.0), (273, 10.0)]}\nsource = 85\ntarget = 226\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 357, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {2: [(679, 20.0)], 679: [(954, 10.0)], 954: [(203, 10.0)], 484: [(679, 20.0)], 203: [(640, 5.0)], 640: [(236, 0.0)], 236: [], 450: [(2, 3.0), (484, 1.0)]}\nsource = 450\ntarget = 236\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 48.0, "source_answer": 48.0}
{"source_row": 358, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {280: [(54, 2.0)], 705: [(54, 2.0)], 54: [(815, 2.0)], 815: [(493, 0.0)], 568: [(54, 2.0)], 493: [], 483: [(280, 2.0), (705, 2.0), (568, 2.0)]}\nsource = 483\ntarget = 493\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 359, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {389: [(419, 30.0)], 419: [(5, 30.0), (541, 30.0)], 541: [(222, 30.0)], 5: [(222, 30.0)], 222: [(101, 15.0)], 101: [(751, 10.0)], 751: [(41, 0.0)], 41: [], 355: [(389, 5.0)]}\nsource = 355\ntarget = 41\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 360, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {529: [(651, 5.0)], 651: [(877, 1.0), (12, 2.0)], 12: [(790, 10.0)], 877: [(790, 10.0)], 790: [(277, 0.0)], 277: [], 168: [(529, 1440.0)]}\nsource = 168\ntarget = 277\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1457.0, "source_answer": 1457.0}
{"source_row": 361, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {972: [(242, 60.0), (905, 60.0)], 242: [(824, 60.0)], 905: [(824, 60.0)], 824: [(695, 60.0)], 695: [(987, 0.0)], 987: [], 781: [(972, 60.0)]}\nsource = 781\ntarget = 987\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 362, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {790: [(700, 43200.0)], 700: [(66, 20160.0)], 66: [(449, 20160.0)], 449: [(67, 43200.0), (531, 20160.0)], 531: [(884, 43200.0)], 67: [(884, 43200.0)], 884: [(629, 0.0)], 629: [], 79: [(790, 20160.0)]}\nsource = 79\ntarget = 629\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 190080.0, "source_answer": 190080.0}
{"source_row": 363, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {673: [(451, 1.0), (699, 1.0)], 451: [(565, 1.0)], 699: [(565, 1.0)], 565: [(768, 5.0)], 768: [(718, 1.0)], 718: [(971, 25.0)], 971: [(710, 0.0)], 710: [], 183: [(673, 0.08)]}\nsource = 183\ntarget = 710\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 33.08, "source_answer": 33.08}
{"source_row": 364, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(937, 360.0)], 578: [(957, 2.0)], 957: [(848, 5.0)], 848: [(601, 2.0)], 937: [(750, 0.0)], 601: [(215, 420.0)], 215: [(750, 0.0)], 750: [], 830: [(863, 10.0), (578, 2880.0)]}\nsource = 830\ntarget = 750\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3309.0, "source_answer": 3309.0}
{"source_row": 365, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {687: [(657, 3.0)], 491: [(921, 10.0)], 921: [(115, 6.0)], 115: [(657, 3.0)], 657: [(759, 0.0)], 759: [], 9: [(687, 10.0), (491, 1.0)]}\nsource = 9\ntarget = 759\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 366, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {97: [(501, 43200.0)], 993: [(373, 240.0)], 373: [(584, 60.0)], 584: [(399, 0.0)], 501: [(373, 240.0)], 399: [], 956: [(97, 60.0), (993, 43200.0)]}\nsource = 956\ntarget = 399\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43560.0, "source_answer": 43560.0}
{"source_row": 367, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {32: [(7, 10.0)], 7: [(365, 1440.0)], 365: [(519, 1.0)], 519: [(485, 5.0), (708, 1.0)], 708: [(727, 30.0)], 485: [(727, 30.0)], 727: [(855, 0.0)], 855: [], 256: [(32, 20.0)]}\nsource = 256\ntarget = 855\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1506.0, "source_answer": 1506.0}
{"source_row": 368, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {455: [(228, 2.0)], 228: [(249, 5.0)], 249: [(947, 60.0)], 947: [(381, 180.0), (639, 300.0), (764, 360.0)], 639: [(328, 0.0)], 381: [(328, 0.0)], 764: [(328, 0.0)], 328: [], 44: [(455, 30.0)]}\nsource = 44\ntarget = 328\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 457.0, "source_answer": 457.0}
{"source_row": 369, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {517: [(101, 2880.0)], 750: [(101, 2880.0)], 101: [(248, 10080.0)], 248: [(733, 2880.0)], 733: [(107, 0.0)], 107: [], 764: [(517, 120.0), (750, 480.0)]}\nsource = 764\ntarget = 107\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16320.0, "source_answer": 16320.0}
{"source_row": 370, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {585: [(655, 43200.0)], 336: [(655, 43200.0)], 655: [(983, 43200.0)], 983: [(300, 0.0)], 626: [(655, 43200.0)], 300: [], 900: [(585, 1440.0), (336, 120.0), (626, 1440.0)]}\nsource = 900\ntarget = 300\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 87840.0, "source_answer": 87840.0}
{"source_row": 371, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {211: [(718, 5.0), (61, 5.0)], 61: [(223, 5.0)], 718: [(42, 0.0)], 223: [(899, 5.0)], 899: [(42, 0.0)], 42: [], 346: [(211, 10.0)]}\nsource = 346\ntarget = 42\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 372, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {566: [(81, 1.0)], 160: [(19, 20.0)], 19: [(570, 3.0)], 570: [(81, 1.0)], 81: [(505, 0.5)], 505: [(925, 2.0)], 925: [(991, 0.0)], 991: [], 507: [(566, 0.75), (160, 1.0)]}\nsource = 507\ntarget = 991\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.5, "source_answer": 27.5}
{"source_row": 373, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {612: [(459, 2.0), (669, 2.0)], 459: [(683, 1.0)], 669: [(683, 1.0)], 683: [(100, 1.0)], 100: [(535, 2.0)], 535: [(547, 15.0)], 547: [(773, 0.0)], 773: [], 206: [(612, 15.0)]}\nsource = 206\ntarget = 773\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.0, "source_answer": 36.0}
{"source_row": 374, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {422: [(600, 20.0)], 864: [(826, 300.0)], 826: [(683, 2.0)], 683: [(506, 0.0)], 600: [(826, 300.0)], 506: [], 581: [(422, 20.0), (864, 5.0)]}\nsource = 581\ntarget = 506\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 342.0, "source_answer": 342.0}
{"source_row": 375, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {461: [(525, 0.03)], 347: [(525, 0.03)], 525: [(871, 0.05)], 871: [(287, 0.03)], 287: [(364, 0.08)], 364: [(562, 0.0)], 562: [], 500: [(461, 0.02), (347, 0.03)]}\nsource = 500\ntarget = 562\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.22, "source_answer": 0.22}
{"source_row": 376, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {762: [(200, 0.08)], 200: [(779, 0.42), (623, 0.42)], 623: [(17, 5.0)], 779: [(17, 5.0)], 17: [(648, 0.0)], 648: [], 310: [(762, 0.25)]}\nsource = 310\ntarget = 648\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.75, "source_answer": 5.75}
{"source_row": 377, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {818: [(456, 30.0)], 456: [(254, 5.0)], 254: [(364, 2.0), (602, 1.0)], 364: [(728, 2.0)], 602: [(728, 2.0)], 728: [(559, 0.0)], 559: [], 996: [(818, 60.0)]}\nsource = 996\ntarget = 559\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 99.0, "source_answer": 99.0}
{"source_row": 378, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {715: [(798, 1.0)], 511: [(906, 0.5)], 906: [(302, 0.17)], 302: [(798, 1.0)], 798: [(259, 10.0)], 259: [(1, 0.0)], 447: [(798, 1.0)], 1: [], 534: [(715, 2.0), (511, 1.0), (447, 1.0)]}\nsource = 534\ntarget = 1\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 379, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {229: [(100, 1.0)], 557: [(200, 1.0), (7, 1.0)], 7: [(621, 0.0)], 200: [(621, 0.0)], 100: [(557, 1.0)], 621: [], 663: [(229, 1.0)]}\nsource = 663\ntarget = 621\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 380, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {600: [(715, 5.0)], 715: [(217, 15.0), (467, 30.0)], 467: [(645, 43200.0)], 217: [(532, 0.0)], 645: [(532, 0.0)], 532: [], 431: [(600, 5.0)]}\nsource = 431\ntarget = 532\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43240.0, "source_answer": 43240.0}
{"source_row": 381, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {551: [(413, 259200.0)], 413: [(983, 259200.0), (245, 259200.0), (685, 259200.0)], 685: [(970, 0.0)], 245: [(970, 0.0)], 983: [(970, 0.0)], 970: [], 750: [(551, 60.0)]}\nsource = 750\ntarget = 970\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 518460.0, "source_answer": 518460.0}
{"source_row": 382, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {150: [(830, 43200.0)], 830: [(743, 5.0), (220, 43200.0)], 220: [(245, 0.0)], 743: [(848, 15.0)], 848: [(245, 0.0)], 245: [], 765: [(150, 86400.0)]}\nsource = 765\ntarget = 245\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 172800.0, "source_answer": 172800.0}
{"source_row": 383, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {197: [(858, 1.0)], 731: [(858, 1.0)], 858: [(174, 2.0)], 174: [(216, 5.0), (244, 2.0)], 244: [(159, 5.0)], 216: [(159, 5.0)], 159: [(526, 0.0)], 526: [], 647: [(197, 0.58), (731, 2.0)]}\nsource = 647\ntarget = 526\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 384, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {631: [(996, 2.0)], 996: [(381, 2.0)], 381: [(595, 15.0), (543, 15.0)], 595: [(210, 43200.0)], 543: [(210, 43200.0)], 210: [(696, 0.0)], 696: [], 371: [(631, 0.5)]}\nsource = 371\ntarget = 696\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43219.5, "source_answer": 43219.5}
{"source_row": 385, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {669: [(881, 5.0)], 103: [(881, 5.0)], 881: [(917, 15.0)], 917: [(873, 5.0)], 873: [(12, 0.0)], 12: [], 289: [(669, 5.0), (103, 10.0)]}\nsource = 289\ntarget = 12\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 386, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(149, 30.0), (218, 0.25)], 149: [(715, 10.0)], 715: [(680, 300.0)], 680: [(210, 0.0)], 218: [(715, 10.0)], 210: [], 366: [(863, 10.0)]}\nsource = 366\ntarget = 210\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 350.0, "source_answer": 350.0}
{"source_row": 387, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {547: [(952, 2.0)], 952: [(240, 1.0), (549, 0.5)], 549: [(744, 2.0)], 240: [(744, 2.0)], 744: [(759, 15.0)], 759: [(889, 10.0)], 889: [(983, 0.0)], 983: [], 290: [(547, 15.0)]}\nsource = 290\ntarget = 983\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 388, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {75: [(613, 3.0)], 485: [(613, 3.0)], 613: [(357, 2.0)], 357: [(454, 5.0)], 454: [(185, 0.0)], 185: [], 199: [(75, 5.0), (485, 2.0)]}\nsource = 199\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 389, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {154: [(246, 2.0)], 688: [(439, 30.0)], 439: [(958, 45.0)], 958: [(344, 0.0)], 246: [(178, 2.0)], 178: [(359, 2.0)], 359: [(958, 45.0)], 344: [], 88: [(154, 2.0), (688, 2.0)]}\nsource = 88\ntarget = 344\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 77.0, "source_answer": 77.0}
{"source_row": 390, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {284: [(570, 1.0)], 570: [(459, 1.0)], 459: [(611, 1.0)], 611: [(123, 0.5), (361, 1.0)], 361: [(442, 5.0)], 123: [(442, 5.0)], 442: [(252, 0.0)], 252: [], 998: [(284, 3.0)]}\nsource = 998\ntarget = 252\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 391, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {290: [(175, 60.0)], 810: [(175, 60.0)], 175: [(560, 60.0)], 560: [(615, 30.0)], 615: [(212, 30.0)], 212: [(766, 60.0)], 766: [(200, 0.0)], 200: [], 315: [(290, 5.0), (810, 1.0)]}\nsource = 315\ntarget = 200\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 245.0, "source_answer": 245.0}
{"source_row": 392, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {748: [(152, 5.0)], 152: [(87, 45.0)], 87: [(869, 3.0)], 307: [(152, 5.0)], 536: [(152, 5.0)], 869: [(820, 5.0)], 820: [(858, 0.0)], 858: [], 703: [(748, 20.0), (307, 20.0), (536, 20.0)]}\nsource = 703\ntarget = 858\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 78.0, "source_answer": 78.0}
{"source_row": 393, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {361: [(196, 525600.0)], 196: [(422, 2.0), (633, 2.0)], 633: [(643, 30.0)], 422: [(643, 30.0)], 643: [(765, 15.0)], 765: [(509, 15.0)], 509: [(869, 0.0)], 869: [], 897: [(361, 1576800.0)]}\nsource = 897\ntarget = 869\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2102462.0, "source_answer": 2102462.0}
{"source_row": 394, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {146: [(169, 525600.0)], 169: [(276, 525600.0), (253, 1051200.0)], 253: [(34, 525600.0)], 276: [(34, 525600.0)], 34: [(185, 0.0)], 185: [], 401: [(146, 1440.0)]}\nsource = 401\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2103840.0, "source_answer": 2103840.0}
{"source_row": 395, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {187: [(310, 0.5)], 310: [(539, 20.0), (534, 10.0)], 534: [(982, 10.0)], 539: [(982, 10.0)], 982: [(636, 0.0)], 636: [], 219: [(187, 20.0)]}\nsource = 219\ntarget = 636\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.5, "source_answer": 50.5}
{"source_row": 396, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {330: [(779, 60.0)], 803: [(235, 86400.0)], 235: [(701, 20.0)], 701: [(930, 0.0)], 779: [(382, 43200.0)], 382: [(930, 0.0)], 930: [], 632: [(330, 172800.0), (803, 120.0)]}\nsource = 632\ntarget = 930\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 216060.0, "source_answer": 216060.0}
{"source_row": 397, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {990: [(267, 60.0), (424, 120.0)], 424: [(417, 60.0)], 267: [(959, 60.0)], 417: [(45, 0.0)], 959: [(45, 0.0)], 45: [], 907: [(990, 120.0)]}\nsource = 907\ntarget = 45\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 398, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {705: [(925, 5.0)], 925: [(249, 30.0)], 249: [(600, 5.0)], 221: [(925, 5.0)], 600: [(643, 0.0)], 643: [], 718: [(705, 20.0), (221, 10.0)]}\nsource = 718\ntarget = 643\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 399, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {923: [(443, 2102400.0), (713, 2102400.0)], 443: [(851, 7200.0)], 713: [(851, 7200.0)], 851: [(723, 7200.0)], 723: [(247, 5.0)], 247: [(999, 30.0)], 999: [(171, 0.0)], 171: [], 740: [(923, 2102400.0)]}\nsource = 740\ntarget = 171\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4219235.0, "source_answer": 4219235.0}
{"source_row": 400, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {595: [(630, 15.0)], 83: [(429, 4.0)], 429: [(577, 2.0)], 577: [(861, 0.0)], 630: [(774, 2.0)], 774: [(83, 5.0), (438, 3.0)], 438: [(429, 4.0)], 861: [], 175: [(595, 5.0)]}\nsource = 175\ntarget = 861\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 33.0, "source_answer": 33.0}
{"source_row": 401, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(501, 2.0), (955, 1.0)], 501: [(948, 2.0)], 955: [(948, 2.0)], 948: [(491, 5.0), (78, 5.0)], 78: [(298, 1.0)], 491: [(298, 1.0)], 298: [(272, 0.0)], 272: [], 523: [(505, 0.58)]}\nsource = 523\ntarget = 272\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.58, "source_answer": 10.58}
{"source_row": 402, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {615: [(460, 20.0)], 460: [(202, 30.0), (13, 30.0)], 13: [(211, 20.0)], 202: [(211, 20.0)], 211: [(617, 20.0)], 617: [(395, 0.0)], 395: [], 805: [(615, 2.0)]}\nsource = 805\ntarget = 395\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 92.0, "source_answer": 92.0}
{"source_row": 403, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {744: [(493, 60.0)], 763: [(493, 60.0)], 493: [(648, 30.0)], 648: [(720, 45.0)], 720: [(346, 0.0)], 346: [], 588: [(744, 20.0), (763, 60.0)]}\nsource = 588\ntarget = 346\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 195.0, "source_answer": 195.0}
{"source_row": 404, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {364: [(359, 1.0)], 772: [(626, 2.0)], 626: [(359, 1.0)], 359: [(902, 2.0)], 902: [(987, 10.0)], 987: [(683, 0.0)], 683: [], 225: [(364, 60.0), (772, 1.0)]}\nsource = 225\ntarget = 683\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 73.0, "source_answer": 73.0}
{"source_row": 405, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {179: [(145, 0.33)], 787: [(145, 0.33)], 145: [(608, 0.17)], 608: [(149, 2.0), (738, 0.5)], 211: [(145, 0.33)], 149: [(841, 0.0)], 738: [(841, 0.0)], 841: [], 792: [(179, 5.0), (787, 3.0), (211, 20.0)]}\nsource = 792\ntarget = 841\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.5, "source_answer": 22.5}
{"source_row": 406, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {602: [(409, 5.0), (233, 5.0)], 409: [(45, 10.0)], 233: [(45, 10.0)], 45: [(500, 10.0)], 500: [(694, 0.0)], 694: [], 721: [(602, 5.0)]}\nsource = 721\ntarget = 694\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 407, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {1: [(871, 5.0)], 871: [(450, 20.0), (547, 30.0)], 547: [(285, 10.0)], 450: [(618, 10.0), (285, 10.0)], 285: [(638, 5.0)], 618: [(638, 5.0)], 638: [(22, 0.0)], 22: [], 629: [(1, 30.0)]}\nsource = 629\ntarget = 22\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0}
{"source_row": 408, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {862: [(331, 1.0)], 17: [(331, 1.0)], 331: [(258, 1.0)], 258: [(766, 1.0)], 766: [(279, 0.0)], 279: [], 945: [(862, 1.0), (17, 1.0)]}\nsource = 945\ntarget = 279\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 409, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {426: [(43, 180.0)], 137: [(43, 180.0)], 43: [(742, 60.0)], 742: [(843, 30.0)], 843: [(948, 1800.0)], 948: [(295, 0.0)], 295: [], 420: [(426, 120.0), (137, 4320.0)]}\nsource = 420\ntarget = 295\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6390.0, "source_answer": 6390.0}
{"source_row": 410, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(253, 0.17)], 253: [(856, 0.08), (671, 0.08)], 671: [(859, 5.0)], 856: [(859, 5.0)], 859: [(169, 0.0)], 169: [], 213: [(881, 0.17)]}\nsource = 213\ntarget = 169\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.42, "source_answer": 5.42}
{"source_row": 411, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {311: [(839, 1.0)], 839: [(945, 0.5)], 945: [(875, 0.33)], 875: [(541, 0.08)], 541: [(655, 1.0), (894, 0.42)], 655: [(512, 0.0)], 894: [(512, 0.0)], 512: [], 983: [(311, 5.0)]}\nsource = 983\ntarget = 512\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.91, "source_answer": 7.91}
{"source_row": 412, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {729: [(211, 60.0)], 618: [(938, 5.0)], 938: [(217, 0.0)], 907: [(938, 5.0)], 211: [(620, 20.0)], 620: [(618, 5.0), (907, 5.0)], 217: [], 709: [(729, 0.5)]}\nsource = 709\ntarget = 217\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.5, "source_answer": 90.5}
{"source_row": 413, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {897: [(351, 0.5)], 409: [(351, 0.5), (479, 1.0)], 479: [(337, 15.0)], 351: [(337, 15.0)], 337: [(71, 0.33)], 71: [(510, 0.0)], 510: [], 9: [(897, 5.0), (409, 2.0)]}\nsource = 9\ntarget = 510\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.83, "source_answer": 20.83}
{"source_row": 414, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {400: [(122, 0.0)], 762: [(908, 43200.0)], 908: [(734, 43200.0)], 734: [(394, 43200.0)], 394: [(122, 0.0)], 122: [], 88: [(400, 43200.0), (762, 43200.0)]}\nsource = 88\ntarget = 122\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 172800.0, "source_answer": 172800.0}
{"source_row": 415, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {926: [(363, 2.0)], 363: [(710, 2.0), (400, 2.0), (687, 2.0)], 400: [(572, 1.0)], 710: [(519, 2.0)], 687: [(996, 0.0)], 572: [(996, 0.0)], 519: [(996, 0.0)], 996: [], 81: [(926, 0.5)]}\nsource = 81\ntarget = 996\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.5, "source_answer": 6.5}
{"source_row": 416, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {520: [(980, 1.0)], 589: [(395, 1.0)], 395: [(69, 1.0)], 69: [(320, 0.0)], 980: [(395, 1.0)], 320: [], 699: [(520, 1.0), (589, 10.0)]}\nsource = 699\ntarget = 320\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 417, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {29: [(726, 5.0)], 757: [(726, 5.0)], 726: [(760, 0.0)], 459: [(726, 5.0)], 321: [(726, 5.0)], 760: [], 963: [(29, 5.0), (757, 2.0), (459, 2.0), (321, 4.0)]}\nsource = 963\ntarget = 760\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 418, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {859: [(163, 5.0)], 163: [(914, 20.0)], 914: [(536, 15.0), (280, 15.0)], 536: [(682, 45.0)], 280: [(682, 45.0)], 682: [(113, 20.0)], 113: [(604, 0.0)], 604: [], 973: [(859, 120.0)]}\nsource = 973\ntarget = 604\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 225.0, "source_answer": 225.0}
{"source_row": 419, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {909: [(693, 1051200.0)], 932: [(693, 1051200.0)], 693: [(193, 60.0)], 193: [(317, 1051200.0)], 317: [(253, 0.0)], 253: [], 391: [(909, 86400.0), (932, 1051200.0)]}\nsource = 391\ntarget = 253\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3153660.0, "source_answer": 3153660.0}
{"source_row": 420, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {572: [(289, 0.17), (946, 3.0)], 289: [(154, 0.08)], 946: [(509, 5.0)], 154: [(751, 25.0), (94, 3.0)], 509: [(986, 0.0)], 751: [(986, 0.0)], 94: [(986, 0.0)], 986: [], 889: [(572, 0.08)]}\nsource = 889\ntarget = 986\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.33, "source_answer": 25.33}
{"source_row": 421, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {0: [(849, 2.0)], 849: [(71, 1.0), (571, 1.0)], 571: [(822, 1.0), (24, 1.0)], 71: [(63, 0.0)], 24: [(63, 0.0)], 822: [(63, 0.0)], 63: [], 948: [(0, 0.5)]}\nsource = 948\ntarget = 63\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.5, "source_answer": 4.5}
{"source_row": 422, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {33: [(263, 0.58), (854, 0.42)], 263: [(509, 0.58)], 854: [(509, 0.58)], 509: [(457, 0.92)], 457: [(325, 0.0)], 325: [], 867: [(33, 0.58)]}\nsource = 867\ntarget = 325\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.66, "source_answer": 2.66}
{"source_row": 423, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {864: [(547, 0.33)], 703: [(523, 0.33)], 523: [(100, 0.17)], 100: [(487, 0.25)], 487: [(379, 0.0)], 547: [(130, 0.17)], 130: [(379, 0.0)], 379: [], 33: [(864, 0.33), (703, 0.5)]}\nsource = 33\ntarget = 379\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.25, "source_answer": 1.25}
{"source_row": 424, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {970: [(281, 0.42), (91, 0.58)], 281: [(538, 0.67)], 91: [(538, 0.67)], 538: [(930, 0.42)], 930: [(679, 0.0)], 679: [], 264: [(970, 0.42)]}\nsource = 264\ntarget = 679\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.09, "source_answer": 2.09}
{"source_row": 425, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {536: [(21, 0.5)], 881: [(794, 0.5)], 794: [(419, 2.0)], 419: [(677, 0.5)], 677: [(741, 1.0)], 21: [(419, 2.0)], 741: [(591, 0.0)], 591: [], 260: [(536, 0.5), (881, 0.5)]}\nsource = 260\ntarget = 591\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.5, "source_answer": 4.5}
{"source_row": 426, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {393: [(762, 1.0)], 407: [(762, 1.0)], 762: [(50, 1.0)], 50: [(819, 1.0)], 819: [(22, 0.0)], 22: [], 969: [(393, 1.0), (407, 1.0)]}\nsource = 969\ntarget = 22\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 427, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {783: [(277, 0.17)], 277: [(97, 0.17)], 97: [(434, 0.17)], 434: [(998, 0.17), (442, 0.5)], 442: [(880, 0.25)], 998: [(880, 0.25)], 880: [(399, 0.0)], 399: [], 525: [(783, 0.5)]}\nsource = 525\ntarget = 399\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.76, "source_answer": 1.76}
{"source_row": 428, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {778: [(90, 0.17)], 693: [(90, 0.17)], 90: [(342, 0.08)], 342: [(250, 0.08)], 250: [(772, 0.0)], 772: [], 403: [(778, 0.17), (693, 0.17)]}\nsource = 403\ntarget = 772\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.5, "source_answer": 0.5}
{"source_row": 429, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {983: [(180, 0.5)], 180: [(218, 2.0), (966, 2.0), (85, 1.0)], 966: [(641, 2.0)], 218: [(148, 1.0)], 85: [(641, 2.0)], 641: [(665, 0.0)], 148: [(665, 0.0)], 665: [], 91: [(983, 0.5)]}\nsource = 91\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 430, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {622: [(767, 0.02), (64, 0.02)], 767: [(187, 0.02)], 64: [(187, 0.02)], 187: [(629, 0.02)], 629: [(583, 0.0)], 583: [], 119: [(622, 0.02)]}\nsource = 119\ntarget = 583\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.08, "source_answer": 0.08}
{"source_row": 431, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {141: [(979, 0.02)], 979: [(415, 0.02)], 415: [(298, 0.02), (514, 0.02)], 298: [(211, 0.0)], 514: [(211, 0.0)], 211: [], 98: [(141, 0.02)]}\nsource = 98\ntarget = 211\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.08, "source_answer": 0.08}
{"source_row": 432, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {288: [(176, 2.0)], 176: [(457, 0.0)], 611: [(457, 0.0)], 457: [], 613: [(288, 5.0), (611, 5.0)]}\nsource = 613\ntarget = 457\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 433, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {172: [(391, 0.0)], 786: [(152, 60.0)], 152: [(391, 0.0)], 391: [], 815: [(172, 120.0), (786, 240.0)]}\nsource = 815\ntarget = 391\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 434, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {853: [(24, 60.0)], 24: [(788, 0.0)], 524: [(788, 0.0)], 788: [], 713: [(853, 30.0), (524, 30.0)]}\nsource = 713\ntarget = 788\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 435, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {617: [(512, 0.0)], 862: [(473, 7200.0)], 473: [(512, 0.0)], 512: [], 567: [(617, 120.0), (862, 30.0)]}\nsource = 567\ntarget = 512\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7230.0, "source_answer": 7230.0}
{"source_row": 436, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {593: [(329, 5.0)], 758: [(329, 5.0)], 329: [(689, 0.0)], 689: [], 15: [(593, 5.0), (758, 10.0)]}\nsource = 15\ntarget = 689\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 437, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {294: [(649, 0.0)], 863: [(942, 0.75)], 942: [(649, 0.0)], 649: [], 329: [(294, 2.0), (863, 1.0)]}\nsource = 329\ntarget = 649\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.0, "source_answer": 2.0}
{"source_row": 438, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {40: [(986, 2.0)], 986: [(885, 0.0)], 904: [(885, 0.0)], 885: [], 537: [(40, 5.0), (904, 1.0)]}\nsource = 537\ntarget = 885\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 439, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {492: [(845, 2.0)], 816: [(845, 2.0)], 845: [(565, 0.0)], 565: [], 153: [(492, 5.0), (816, 2.0)]}\nsource = 153\ntarget = 565\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 440, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {808: [(765, 2.0)], 333: [(765, 2.0)], 765: [(228, 0.0)], 228: [], 559: [(808, 2.0), (333, 2.0)]}\nsource = 559\ntarget = 228\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 441, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {383: [(782, 0.0)], 480: [(67, 15.0)], 67: [(782, 0.0)], 782: [], 443: [(383, 5.0), (480, 30.0)]}\nsource = 443\ntarget = 782\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 442, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {785: [(891, 15.0), (768, 10.0)], 891: [(717, 0.0)], 768: [(717, 0.0)], 717: [], 434: [(785, 60.0)]}\nsource = 434\ntarget = 717\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 443, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(541, 10.0), (860, 20.0)], 541: [(690, 0.0)], 860: [(690, 0.0)], 690: [], 545: [(881, 15.0)]}\nsource = 545\ntarget = 690\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 444, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {624: [(734, 0.0)], 612: [(126, 20.0)], 126: [(734, 0.0)], 734: [], 692: [(624, 5.0), (612, 10.0)]}\nsource = 692\ntarget = 734\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 445, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {601: [(213, 0.0)], 710: [(73, 10.0)], 73: [(213, 0.0)], 213: [], 445: [(601, 5.0), (710, 5.0)]}\nsource = 445\ntarget = 213\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 446, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {913: [(460, 0.0)], 455: [(913, 15.0)], 749: [(913, 15.0)], 460: [], 25: [(455, 20.0), (749, 20.0)]}\nsource = 25\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 447, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {560: [(98, 0.03)], 638: [(98, 0.03)], 98: [(994, 0.0)], 994: [], 722: [(560, 0.08), (638, 0.08)]}\nsource = 722\ntarget = 994\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.11, "source_answer": 0.11}
{"source_row": 448, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {89: [(66, 15.0)], 66: [(998, 0.0)], 484: [(998, 0.0)], 998: [], 810: [(89, 5.0), (484, 10.0)]}\nsource = 810\ntarget = 998\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 449, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {354: [(924, 0.0)], 90: [(919, 15.0)], 919: [(924, 0.0)], 924: [], 659: [(354, 5.0), (90, 10.0)]}\nsource = 659\ntarget = 924\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 450, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {2: [(584, 60.0), (14, 30.0)], 584: [(876, 0.0)], 14: [(876, 0.0)], 876: [], 32: [(2, 30240.0)]}\nsource = 32\ntarget = 876\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30300.0, "source_answer": 30300.0}
{"source_row": 451, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {439: [(17, 20.0)], 417: [(17, 20.0)], 17: [(71, 0.0)], 71: [], 925: [(439, 5.0), (417, 2.0)]}\nsource = 925\ntarget = 71\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 452, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {542: [(99, 0.0)], 883: [(542, 15.0)], 700: [(542, 15.0)], 99: [], 693: [(883, 20.0), (700, 30.0)]}\nsource = 693\ntarget = 99\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 453, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {732: [(330, 0.0)], 983: [(849, 777600.0)], 849: [(330, 0.0)], 330: [], 328: [(732, 80640.0), (983, 345600.0)]}\nsource = 328\ntarget = 330\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1123200.0, "source_answer": 1123200.0}
{"source_row": 454, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {576: [(101, 20160.0), (463, 20160.0)], 101: [(612, 0.0)], 463: [(612, 0.0)], 612: [], 470: [(576, 14400.0)]}\nsource = 470\ntarget = 612\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34560.0, "source_answer": 34560.0}
{"source_row": 455, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {367: [(14, 3.0)], 14: [(27, 0.0)], 554: [(27, 0.0)], 27: [], 287: [(367, 2.0), (554, 5.0)]}\nsource = 287\ntarget = 27\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 456, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {194: [(127, 10.0), (157, 15.0)], 127: [(975, 0.0)], 157: [(975, 0.0)], 975: [], 967: [(194, 5.0)]}\nsource = 967\ntarget = 975\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 457, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {629: [(778, 0.0)], 123: [(629, 120.0)], 223: [(778, 0.0)], 778: [], 801: [(123, 120.0), (223, 120.0)]}\nsource = 801\ntarget = 778\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 458, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {489: [(900, 0.08), (500, 0.08)], 900: [(503, 0.0)], 500: [(503, 0.0)], 503: [], 984: [(489, 0.03)]}\nsource = 984\ntarget = 503\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.11, "source_answer": 0.11}
{"source_row": 459, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {245: [(537, 3.0)], 622: [(537, 3.0)], 537: [(561, 0.0)], 561: [], 670: [(245, 2.0), (622, 2.0)]}\nsource = 670\ntarget = 561\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 460, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {753: [(987, 3.0), (408, 4.0)], 987: [(47, 0.0)], 408: [(47, 0.0)], 47: [], 447: [(753, 2.0)]}\nsource = 447\ntarget = 47\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 461, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {101: [(800, 0.0)], 126: [(973, 20.0)], 973: [(800, 0.0)], 800: [], 372: [(101, 5.0), (126, 15.0)]}\nsource = 372\ntarget = 800\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 462, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {181: [(808, 0.0)], 881: [(730, 30.0), (181, 120.0)], 730: [(808, 0.0)], 808: [], 924: [(881, 120.0)]}\nsource = 924\ntarget = 808\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 463, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {484: [(723, 0.0)], 273: [(292, 4320.0), (484, 86400.0)], 292: [(723, 0.0)], 723: [], 785: [(273, 20160.0)]}\nsource = 785\ntarget = 723\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 106560.0, "source_answer": 106560.0}
{"source_row": 464, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {351: [(956, 4320.0), (518, 2880.0)], 956: [(713, 0.0)], 518: [(713, 0.0)], 713: [], 218: [(351, 2880.0)]}\nsource = 218\ntarget = 713\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7200.0, "source_answer": 7200.0}
{"source_row": 465, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(606, 10080.0)], 606: [(198, 0.0)], 201: [(198, 0.0)], 198: [], 0: [(316, 1440.0), (201, 43200.0)]}\nsource = 0\ntarget = 198\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43200.0, "source_answer": 43200.0}
{"source_row": 466, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {783: [(647, 30.0)], 930: [(647, 30.0)], 647: [(899, 0.0)], 899: [], 920: [(783, 10.0), (930, 5.0)]}\nsource = 920\ntarget = 899\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 467, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {480: [(233, 0.0)], 852: [(480, 60.0)], 779: [(233, 0.0)], 233: [], 297: [(852, 30.0), (779, 120.0)]}\nsource = 297\ntarget = 233\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 468, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {369: [(326, 30.0), (950, 20160.0)], 326: [(427, 0.0)], 950: [(427, 0.0)], 427: [], 107: [(369, 60.0)]}\nsource = 107\ntarget = 427\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20220.0, "source_answer": 20220.0}
{"source_row": 469, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {762: [(502, 0.0)], 819: [(409, 0.08), (762, 0.08)], 409: [(502, 0.0)], 502: [], 782: [(819, 0.17)]}\nsource = 782\ntarget = 502\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.25, "source_answer": 0.25}
{"source_row": 470, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {16: [(611, 5.0)], 675: [(611, 5.0)], 611: [(876, 0.0)], 876: [], 976: [(16, 5.0), (675, 1.0)]}\nsource = 976\ntarget = 876\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 471, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {767: [(217, 10.0)], 216: [(217, 10.0)], 217: [(377, 0.0)], 377: [], 490: [(767, 30.0), (216, 20.0)]}\nsource = 490\ntarget = 377\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 472, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(994, 0.0)], 638: [(814, 2880.0), (458, 2880.0)], 814: [(994, 0.0)], 994: [], 112: [(638, 4320.0)]}\nsource = 112\ntarget = 994\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7200.0, "source_answer": 7200.0}
{"source_row": 473, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {595: [(301, 3.0)], 301: [(858, 0.0)], 815: [(858, 0.0)], 858: [], 743: [(595, 2.0), (815, 1.0)]}\nsource = 743\ntarget = 858\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 474, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {652: [(63, 15.0)], 63: [(484, 0.0)], 114: [(484, 0.0)], 484: [], 731: [(652, 30.0), (114, 30.0)]}\nsource = 731\ntarget = 484\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 475, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {887: [(235, 0.0)], 725: [(887, 5.0)], 410: [(887, 5.0)], 235: [], 608: [(725, 3.0), (410, 10.0)]}\nsource = 608\ntarget = 235\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 476, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {247: [(228, 0.0)], 80: [(182, 120.0), (247, 30.0)], 182: [(228, 0.0)], 228: [], 771: [(80, 60.0)]}\nsource = 771\ntarget = 228\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 477, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {454: [(297, 0.0)], 991: [(793, 15.0), (454, 60.0)], 793: [(297, 0.0)], 297: [], 199: [(991, 60.0)]}\nsource = 199\ntarget = 297\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 478, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {140: [(870, 5.0)], 962: [(870, 5.0)], 870: [(629, 0.0)], 629: [], 260: [(140, 10.0), (962, 15.0)]}\nsource = 260\ntarget = 629\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 479, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {639: [(536, 0.0)], 940: [(639, 120.0)], 588: [(639, 120.0)], 536: [], 66: [(940, 120.0), (588, 1440.0)]}\nsource = 66\ntarget = 536\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1560.0, "source_answer": 1560.0}
{"source_row": 480, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {360: [(609, 1440.0), (861, 120.0)], 609: [(332, 0.0)], 861: [(332, 0.0)], 332: [], 621: [(360, 20160.0)]}\nsource = 621\ntarget = 332\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21600.0, "source_answer": 21600.0}
{"source_row": 481, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {445: [(387, 20160.0), (43, 43200.0)], 387: [(723, 0.0)], 43: [(723, 0.0)], 723: [], 786: [(445, 2880.0)]}\nsource = 786\ntarget = 723\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46080.0, "source_answer": 46080.0}
{"source_row": 482, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {924: [(903, 0.0)], 276: [(954, 15.0)], 954: [(903, 0.0)], 903: [], 899: [(924, 5.0), (276, 10.0)]}\nsource = 899\ntarget = 903\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 483, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {260: [(928, 0.0)], 735: [(260, 5.0)], 647: [(260, 5.0)], 928: [], 892: [(735, 10.0), (647, 15.0)]}\nsource = 892\ntarget = 928\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 484, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {82: [(88, 20.0)], 88: [(883, 0.0)], 238: [(883, 0.0)], 883: [], 123: [(82, 30.0), (238, 20.0)]}\nsource = 123\ntarget = 883\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 485, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {377: [(690, 30.0), (252, 45.0)], 690: [(121, 0.0)], 252: [(121, 0.0)], 121: [], 698: [(377, 5.0)]}\nsource = 698\ntarget = 121\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 486, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {939: [(533, 0.0)], 264: [(939, 5.0)], 776: [(533, 0.0)], 533: [], 794: [(264, 2.0), (776, 3.0)]}\nsource = 794\ntarget = 533\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 487, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {342: [(808, 0.0)], 90: [(342, 10.0)], 948: [(342, 10.0)], 808: [], 489: [(90, 5.0), (948, 15.0)]}\nsource = 489\ntarget = 808\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 488, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {768: [(485, 30.0)], 310: [(393, 0.0)], 485: [(393, 0.0)], 393: [], 227: [(768, 120.0), (310, 120.0)]}\nsource = 227\ntarget = 393\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 489, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {995: [(423, 30.0), (336, 15.0)], 423: [(757, 0.0)], 336: [(757, 0.0)], 757: [], 298: [(995, 10.0)]}\nsource = 298\ntarget = 757\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 490, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {62: [(57, 20.0), (772, 45.0)], 57: [(847, 0.0)], 772: [(847, 0.0)], 847: [], 123: [(62, 10.0)]}\nsource = 123\ntarget = 847\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 491, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {858: [(970, 3153600.0), (388, 518400.0)], 970: [(638, 0.0)], 388: [(638, 0.0)], 638: [], 44: [(858, 2102400.0)]}\nsource = 44\ntarget = 638\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5256000.0, "source_answer": 5256000.0}
{"source_row": 492, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {994: [(700, 0.0)], 469: [(994, 5.0)], 927: [(994, 5.0)], 700: [], 689: [(469, 10.0), (927, 15.0)]}\nsource = 689\ntarget = 700\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 493, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {445: [(781, 2880.0)], 896: [(731, 0.0)], 781: [(731, 0.0)], 731: [], 394: [(445, 120.0), (896, 20160.0)]}\nsource = 394\ntarget = 731\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 494, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {867: [(824, 2.0)], 773: [(824, 2.0)], 824: [(928, 0.0)], 928: [], 901: [(867, 20.0), (773, 5.0)]}\nsource = 901\ntarget = 928\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 495, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {420: [(819, 0.0)], 295: [(420, 1.0)], 174: [(819, 0.0)], 819: [], 717: [(295, 10.0), (174, 30240.0)]}\nsource = 717\ntarget = 819\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30240.0, "source_answer": 30240.0}
{"source_row": 496, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {13: [(657, 0.0)], 775: [(725, 360.0)], 725: [(657, 0.0)], 657: [], 85: [(13, 10.0), (775, 5.0)]}\nsource = 85\ntarget = 657\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 365.0, "source_answer": 365.0}
{"source_row": 497, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {925: [(116, 0.0)], 162: [(116, 0.0)], 236: [(162, 259200.0), (925, 40320.0)], 116: [], 63: [(236, 20160.0)]}\nsource = 63\ntarget = 116\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 279360.0, "source_answer": 279360.0}
{"source_row": 498, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {176: [(570, 0.0)], 493: [(834, 2.0)], 834: [(570, 0.0)], 570: [], 536: [(176, 1.0), (493, 3.0)]}\nsource = 536\ntarget = 570\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 499, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {141: [(487, 40320.0), (115, 172800.0)], 487: [(437, 0.0)], 115: [(437, 0.0)], 437: [], 405: [(141, 20160.0)]}\nsource = 405\ntarget = 437\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 192960.0, "source_answer": 192960.0}
{"source_row": 500, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {596: [(424, 10.0)], 974: [(424, 10.0)], 424: [(558, 0.0)], 558: [], 939: [(596, 5.0), (974, 2.0)]}\nsource = 939\ntarget = 558\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 501, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {554: [(257, 0.0)], 278: [(786, 2102400.0)], 786: [(257, 0.0)], 257: [], 588: [(554, 20160.0), (278, 20160.0)]}\nsource = 588\ntarget = 257\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2122560.0, "source_answer": 2122560.0}
{"source_row": 502, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {289: [(996, 0.0)], 608: [(289, 1.0)], 52: [(289, 1.0)], 996: [], 910: [(608, 0.5), (52, 1.0)]}\nsource = 910\ntarget = 996\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.0, "source_answer": 2.0}
{"source_row": 503, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {381: [(499, 4320.0)], 499: [(125, 0.0)], 457: [(125, 0.0)], 125: [], 818: [(381, 1440.0), (457, 2880.0)]}\nsource = 818\ntarget = 125\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5760.0, "source_answer": 5760.0}
{"source_row": 504, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {521: [(318, 0.0)], 474: [(521, 1440.0)], 752: [(521, 1440.0)], 318: [], 381: [(474, 15.0), (752, 5.0)]}\nsource = 381\ntarget = 318\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1455.0, "source_answer": 1455.0}
{"source_row": 505, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {44: [(693, 0.0)], 358: [(44, 10.0)], 672: [(44, 10.0)], 693: [], 448: [(358, 20.0), (672, 20.0)]}\nsource = 448\ntarget = 693\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 506, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {389: [(565, 0.0)], 570: [(389, 5.0)], 759: [(565, 0.0)], 565: [], 438: [(570, 3.0), (759, 3.0)]}\nsource = 438\ntarget = 565\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 507, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {276: [(998, 60.0)], 998: [(258, 0.0)], 194: [(258, 0.0)], 258: [], 760: [(276, 180.0), (194, 15.0)]}\nsource = 760\ntarget = 258\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 508, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {239: [(293, 0.0)], 470: [(937, 10.0)], 937: [(293, 0.0)], 293: [], 320: [(239, 60.0), (470, 180.0)]}\nsource = 320\ntarget = 293\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 190.0, "source_answer": 190.0}
{"source_row": 509, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {722: [(528, 0.0)], 926: [(793, 40320.0)], 793: [(528, 0.0)], 528: [], 113: [(722, 1036800.0), (926, 20160.0)]}\nsource = 113\ntarget = 528\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1036800.0, "source_answer": 1036800.0}
{"source_row": 510, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {153: [(292, 15.0), (184, 10.0)], 292: [(981, 0.0)], 184: [(981, 0.0)], 981: [], 957: [(153, 120.0)]}\nsource = 957\ntarget = 981\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 511, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {396: [(863, 2.0)], 118: [(863, 2.0)], 863: [(762, 0.0)], 762: [], 591: [(396, 5.0), (118, 5.0)]}\nsource = 591\ntarget = 762\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 512, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {145: [(259, 120.0)], 259: [(446, 0.0)], 843: [(446, 0.0)], 446: [], 318: [(145, 60.0), (843, 30.0)]}\nsource = 318\ntarget = 446\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 513, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {20: [(388, 0.0)], 584: [(459, 2.0)], 459: [(388, 0.0)], 388: [], 129: [(20, 2.0), (584, 2.0)]}\nsource = 129\ntarget = 388\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 514, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {796: [(937, 2.0)], 468: [(937, 2.0)], 937: [(975, 0.0)], 975: [], 346: [(796, 5.0), (468, 3.0)]}\nsource = 346\ntarget = 975\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 515, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {193: [(305, 15.0), (868, 20.0)], 305: [(541, 0.0)], 868: [(541, 0.0)], 541: [], 64: [(193, 10.0)]}\nsource = 64\ntarget = 541\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 516, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {85: [(79, 45.0), (670, 20.0)], 79: [(613, 0.0)], 670: [(613, 0.0)], 613: [], 961: [(85, 30.0)]}\nsource = 961\ntarget = 613\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 517, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {761: [(676, 10.0), (436, 3.0)], 676: [(310, 0.0)], 436: [(310, 0.0)], 310: [], 733: [(761, 5.0)]}\nsource = 733\ntarget = 310\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 518, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {368: [(289, 5.0), (485, 2.0)], 289: [(572, 0.0)], 485: [(572, 0.0)], 572: [], 685: [(368, 60.0)]}\nsource = 685\ntarget = 572\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 519, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {266: [(697, 0.0)], 357: [(697, 0.0)], 869: [(357, 3.0)], 697: [], 643: [(266, 5.0), (869, 2.0)]}\nsource = 643\ntarget = 697\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 520, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {561: [(268, 3.0)], 867: [(268, 3.0)], 268: [(662, 0.0)], 662: [], 171: [(561, 5.0), (867, 3.0)]}\nsource = 171\ntarget = 662\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 521, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {168: [(650, 30.0)], 457: [(650, 30.0)], 650: [(167, 0.0)], 167: [], 696: [(168, 180.0), (457, 5.0)]}\nsource = 696\ntarget = 167\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 210.0, "source_answer": 210.0}
{"source_row": 522, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {137: [(292, 0.0)], 571: [(174, 5.0), (137, 5.0)], 174: [(292, 0.0)], 292: [], 822: [(571, 10.0)]}\nsource = 822\ntarget = 292\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 523, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {801: [(151, 0.0)], 881: [(223, 20.0)], 223: [(151, 0.0)], 151: [], 880: [(801, 10.0), (881, 15.0)]}\nsource = 880\ntarget = 151\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 524, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {382: [(233, 1051200.0)], 233: [(398, 0.0)], 332: [(398, 0.0)], 398: [], 374: [(382, 2102400.0), (332, 518400.0)]}\nsource = 374\ntarget = 398\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3153600.0, "source_answer": 3153600.0}
{"source_row": 525, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {848: [(8, 0.0)], 445: [(350, 5.0)], 350: [(8, 0.0)], 8: [], 632: [(848, 1.0), (445, 3.0)]}\nsource = 632\ntarget = 8\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 526, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {693: [(795, 60.0), (697, 300.0)], 795: [(933, 0.0)], 697: [(933, 0.0)], 933: [], 145: [(693, 120.0)]}\nsource = 145\ntarget = 933\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 420.0, "source_answer": 420.0}
{"source_row": 527, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {430: [(850, 30.0)], 889: [(850, 30.0)], 850: [(639, 0.0)], 639: [], 983: [(430, 60.0), (889, 120.0)]}\nsource = 983\ntarget = 639\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 528, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {664: [(479, 0.0)], 572: [(479, 0.0)], 614: [(572, 43200.0), (664, 43200.0)], 479: [], 329: [(614, 20160.0)]}\nsource = 329\ntarget = 479\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63360.0, "source_answer": 63360.0}
{"source_row": 529, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {981: [(729, 0.0)], 90: [(314, 129600.0)], 314: [(729, 0.0)], 729: [], 441: [(981, 129600.0), (90, 86400.0)]}\nsource = 441\ntarget = 729\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 216000.0, "source_answer": 216000.0}
{"source_row": 530, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {598: [(33, 5.0)], 67: [(12, 1.0)], 33: [(790, 0.0)], 12: [(790, 0.0)], 790: [], 23: [(598, 10.0), (67, 30.0)]}\nsource = 23\ntarget = 790\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0}
{"source_row": 531, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {865: [(903, 15.0)], 169: [(923, 0.0)], 903: [(923, 0.0)], 90: [(923, 0.0)], 923: [], 266: [(865, 30.0), (169, 5.0), (90, 20.0)]}\nsource = 266\ntarget = 923\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 532, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {261: [(112, 60.0)], 620: [(112, 60.0)], 182: [(196, 0.0)], 112: [(196, 0.0)], 196: [], 306: [(261, 60.0), (620, 30.0), (182, 120.0)]}\nsource = 306\ntarget = 196\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 533, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {452: [(110, 2.0)], 110: [(836, 1.0)], 836: [(53, 0.0)], 350: [(53, 0.0)], 53: [], 478: [(452, 5.0), (350, 30.0)]}\nsource = 478\ntarget = 53\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 534, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {657: [(38, 20.0), (480, 30.0), (194, 120.0)], 480: [(57, 0.0)], 194: [(57, 0.0)], 38: [(57, 0.0)], 57: [], 749: [(657, 120.0)]}\nsource = 749\ntarget = 57\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 535, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {292: [(839, 15.0)], 839: [(814, 0.0)], 998: [(814, 0.0)], 814: [], 183: [(292, 30.0), (998, 10.0)]}\nsource = 183\ntarget = 814\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 536, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {296: [(767, 2.0)], 767: [(464, 10.0), (253, 1.0)], 253: [(830, 0.0)], 464: [(830, 0.0)], 830: [], 558: [(296, 5.0)]}\nsource = 558\ntarget = 830\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 537, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {940: [(751, 0.0)], 490: [(940, 30.0)], 626: [(940, 30.0)], 453: [(940, 30.0)], 751: [], 389: [(490, 15.0), (626, 15.0), (453, 15.0)]}\nsource = 389\ntarget = 751\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 538, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {874: [(750, 20.0), (551, 10.0), (732, 15.0)], 551: [(728, 0.0)], 732: [(728, 0.0)], 750: [(728, 0.0)], 728: [], 673: [(874, 30.0)]}\nsource = 673\ntarget = 728\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 539, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(727, 2.0)], 679: [(727, 2.0)], 693: [(645, 0.0)], 727: [(693, 8.0)], 645: [], 966: [(316, 10.0), (679, 5.0)]}\nsource = 966\ntarget = 645\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 540, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {938: [(460, 2880.0)], 460: [(478, 0.0)], 262: [(478, 0.0)], 912: [(478, 0.0)], 478: [], 277: [(938, 40320.0), (262, 2880.0), (912, 30240.0)]}\nsource = 277\ntarget = 478\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43200.0, "source_answer": 43200.0}
{"source_row": 541, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {193: [(342, 45.0)], 275: [(342, 45.0)], 342: [(903, 10.0)], 903: [(589, 0.0)], 589: [], 374: [(193, 30.0), (275, 15.0)]}\nsource = 374\ntarget = 589\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 542, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {692: [(210, 30.0)], 350: [(558, 1.0)], 558: [(210, 30.0)], 210: [(332, 0.0)], 332: [], 103: [(692, 5.0), (350, 2.0)]}\nsource = 103\ntarget = 332\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 543, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {774: [(862, 2.0), (915, 2.0)], 862: [(7, 0.0)], 915: [(7, 0.0)], 7: [], 491: [(774, 5.0)]}\nsource = 491\ntarget = 7\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 544, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {385: [(346, 15.0)], 346: [(963, 0.0)], 142: [(963, 0.0)], 104: [(963, 0.0)], 963: [], 51: [(385, 10.0), (142, 15.0), (104, 10.0)]}\nsource = 51\ntarget = 963\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 545, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {814: [(513, 0.0)], 703: [(329, 60.0)], 329: [(513, 0.0)], 830: [(513, 0.0)], 513: [], 109: [(814, 120.0), (703, 120.0), (830, 60.0)]}\nsource = 109\ntarget = 513\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 546, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {908: [(721, 3.0)], 529: [(721, 3.0)], 721: [(55, 0.0)], 55: [], 405: [(908, 5.0), (529, 2.0)]}\nsource = 405\ntarget = 55\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 547, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {789: [(510, 0.0)], 134: [(510, 0.0)], 14: [(510, 0.0)], 415: [(134, 10.0), (789, 5.0), (14, 15.0)], 510: [], 214: [(415, 5.0)]}\nsource = 214\ntarget = 510\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 548, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {11: [(938, 30.0)], 938: [(599, 30.0)], 640: [(281, 0.0)], 599: [(281, 0.0)], 281: [], 435: [(11, 10.0), (640, 20.0)]}\nsource = 435\ntarget = 281\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 549, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {736: [(420, 10.0)], 477: [(420, 10.0)], 420: [(441, 80640.0)], 441: [(916, 0.0)], 916: [], 63: [(736, 15.0), (477, 30.0)]}\nsource = 63\ntarget = 916\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80680.0, "source_answer": 80680.0}
{"source_row": 550, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {866: [(250, 0.0)], 202: [(250, 0.0)], 494: [(250, 0.0)], 447: [(202, 60.0), (494, 30.0)], 250: [], 127: [(866, 30.0), (447, 60.0)]}\nsource = 127\ntarget = 250\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 551, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {31: [(587, 7.0)], 338: [(151, 0.0)], 587: [(151, 0.0)], 742: [(151, 0.0)], 151: [], 227: [(31, 5.0), (338, 10.0), (742, 3.0)]}\nsource = 227\ntarget = 151\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 552, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {289: [(184, 20.0)], 455: [(803, 0.0)], 184: [(803, 0.0)], 804: [(455, 30.0), (289, 10.0)], 803: [], 960: [(804, 5.0)]}\nsource = 960\ntarget = 803\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 553, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {31: [(685, 30.0)], 775: [(685, 30.0)], 685: [(686, 0.0)], 686: [], 284: [(31, 60.0), (775, 120.0)]}\nsource = 284\ntarget = 686\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 554, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {737: [(567, 0.0)], 683: [(737, 30.0)], 952: [(737, 30.0)], 567: [], 633: [(683, 60.0), (952, 60.0)]}\nsource = 633\ntarget = 567\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 555, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {434: [(927, 0.0)], 93: [(123, 60.0), (434, 60.0)], 123: [(927, 0.0)], 927: [], 922: [(93, 20160.0)]}\nsource = 922\ntarget = 927\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20220.0, "source_answer": 20220.0}
{"source_row": 556, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {989: [(640, 0.0)], 740: [(722, 30.0), (756, 15.0)], 756: [(640, 0.0)], 722: [(640, 0.0)], 640: [], 998: [(989, 30.0), (740, 60.0)]}\nsource = 998\ntarget = 640\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 557, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {753: [(176, 0.0)], 117: [(753, 60.0)], 893: [(753, 60.0)], 176: [], 751: [(117, 30240.0), (893, 240.0)]}\nsource = 751\ntarget = 176\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30300.0, "source_answer": 30300.0}
{"source_row": 558, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {984: [(620, 0.0)], 484: [(984, 60.0)], 374: [(314, 60.0)], 314: [(620, 0.0)], 620: [], 665: [(484, 30.0), (374, 60.0)]}\nsource = 665\ntarget = 620\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 559, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {205: [(223, 240.0), (133, 180.0), (513, 180.0)], 133: [(891, 0.0)], 513: [(891, 0.0)], 223: [(891, 0.0)], 891: [], 732: [(205, 30.0)]}\nsource = 732\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 270.0, "source_answer": 270.0}
{"source_row": 560, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {332: [(521, 3.0)], 692: [(521, 3.0)], 521: [(697, 0.0)], 697: [], 610: [(332, 5.0), (692, 3.0)]}\nsource = 610\ntarget = 697\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 561, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {222: [(462, 1440.0), (315, 4320.0)], 462: [(383, 0.0)], 315: [(383, 0.0)], 139: [(383, 0.0)], 383: [], 619: [(222, 40320.0), (139, 40320.0)]}\nsource = 619\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 44640.0, "source_answer": 44640.0}
{"source_row": 562, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {373: [(633, 0.0)], 272: [(775, 60.0), (911, 30.0)], 911: [(633, 0.0)], 775: [(633, 0.0)], 633: [], 919: [(373, 20.0), (272, 15.0)]}\nsource = 919\ntarget = 633\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 563, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {469: [(557, 0.0)], 343: [(41, 60.0)], 41: [(557, 0.0)], 557: [], 637: [(469, 30.0), (343, 60.0)]}\nsource = 637\ntarget = 557\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 564, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {955: [(207, 0.0)], 566: [(11, 5.0), (955, 30.0)], 11: [(207, 0.0)], 207: [], 187: [(566, 10.0)]}\nsource = 187\ntarget = 207\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 565, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {653: [(910, 45.0), (516, 60.0), (271, 30.0)], 516: [(455, 0.0)], 271: [(455, 0.0)], 910: [(455, 0.0)], 455: [], 593: [(653, 15.0)]}\nsource = 593\ntarget = 455\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 566, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {982: [(492, 30.0)], 492: [(970, 30.0)], 970: [(838, 0.0)], 3: [(838, 0.0)], 838: [], 211: [(982, 5.0), (3, 10.0)]}\nsource = 211\ntarget = 838\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 567, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {646: [(691, 10.0)], 691: [(667, 15.0)], 667: [(373, 0.0)], 980: [(667, 15.0)], 373: [], 972: [(646, 5.0), (980, 30.0)]}\nsource = 972\ntarget = 373\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 568, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {662: [(987, 5.0), (402, 10.0)], 456: [(987, 5.0), (402, 10.0)], 402: [(10, 0.0)], 987: [(10, 0.0)], 10: [], 781: [(662, 5.0), (456, 1.0)]}\nsource = 781\ntarget = 10\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 569, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {9: [(280, 0.0)], 2: [(778, 3.0)], 778: [(280, 0.0)], 280: [], 242: [(9, 2.0), (2, 5.0)]}\nsource = 242\ntarget = 280\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 570, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {89: [(399, 20.0)], 472: [(399, 20.0)], 280: [(661, 0.0)], 399: [(280, 60480.0)], 661: [], 906: [(89, 20160.0), (472, 15.0)]}\nsource = 906\ntarget = 661\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80660.0, "source_answer": 80660.0}
{"source_row": 571, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {853: [(939, 259200.0), (223, 86400.0), (897, 129600.0)], 223: [(222, 0.0)], 897: [(222, 0.0)], 939: [(222, 0.0)], 222: [], 936: [(853, 40320.0)]}\nsource = 936\ntarget = 222\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 299520.0, "source_answer": 299520.0}
{"source_row": 572, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {28: [(748, 240.0)], 748: [(386, 259200.0), (906, 60480.0)], 906: [(833, 0.0)], 386: [(833, 0.0)], 833: [], 74: [(28, 20160.0)]}\nsource = 74\ntarget = 833\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 279600.0, "source_answer": 279600.0}
{"source_row": 573, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {50: [(138, 0.0)], 793: [(328, 30.0), (50, 120.0), (897, 15.0)], 897: [(138, 0.0)], 328: [(138, 0.0)], 138: [], 386: [(793, 480.0)]}\nsource = 386\ntarget = 138\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 600.0, "source_answer": 600.0}
{"source_row": 574, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {943: [(381, 10.0)], 381: [(108, 0.0)], 102: [(943, 5.0)], 485: [(943, 5.0)], 108: [], 365: [(102, 30.0), (485, 60.0)]}\nsource = 365\ntarget = 108\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 575, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {158: [(791, 10080.0)], 763: [(264, 0.0)], 791: [(264, 0.0)], 463: [(264, 0.0)], 264: [], 413: [(158, 1440.0), (763, 60.0), (463, 1440.0)]}\nsource = 413\ntarget = 264\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11520.0, "source_answer": 11520.0}
{"source_row": 576, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {541: [(297, 2.0)], 86: [(774, 10.0)], 774: [(297, 2.0)], 297: [(834, 0.0)], 834: [], 165: [(541, 15.0), (86, 120.0)]}\nsource = 165\ntarget = 834\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 132.0, "source_answer": 132.0}
{"source_row": 577, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {402: [(730, 60.0)], 96: [(730, 60.0)], 730: [(748, 0.0)], 748: [], 665: [(402, 30.0), (96, 15.0)]}\nsource = 665\ntarget = 748\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 578, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {712: [(763, 2.0)], 763: [(350, 0.0)], 320: [(537, 5.0)], 537: [(763, 2.0)], 350: [], 854: [(712, 5.0), (320, 10.0)]}\nsource = 854\ntarget = 350\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 579, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {880: [(905, 10.0)], 96: [(183, 5.0)], 183: [(905, 10.0)], 905: [(886, 0.0)], 886: [], 640: [(880, 15.0), (96, 5.0)]}\nsource = 640\ntarget = 886\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 580, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {381: [(901, 5.0)], 835: [(901, 5.0)], 901: [(769, 10.0)], 769: [(805, 0.0)], 805: [], 795: [(381, 2.0), (835, 5.0)]}\nsource = 795\ntarget = 805\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 581, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {462: [(981, 0.0)], 812: [(981, 0.0)], 975: [(981, 0.0)], 93: [(812, 20.0), (462, 30.0), (975, 15.0)], 981: [], 162: [(93, 10.0)]}\nsource = 162\ntarget = 981\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 582, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {774: [(17, 5.0)], 0: [(17, 5.0)], 17: [(58, 3.0)], 58: [(589, 0.0)], 589: [], 911: [(774, 1.0), (0, 2.0)]}\nsource = 911\ntarget = 589\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 583, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {758: [(63, 20.0)], 63: [(726, 20.0), (826, 10.0)], 826: [(404, 0.0)], 726: [(404, 0.0)], 404: [], 525: [(758, 30.0)]}\nsource = 525\ntarget = 404\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 584, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {852: [(245, 20160.0)], 98: [(245, 20160.0)], 108: [(245, 20160.0)], 245: [(448, 0.0)], 448: [], 76: [(852, 30.0), (98, 30.0), (108, 20.0)]}\nsource = 76\ntarget = 448\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20190.0, "source_answer": 20190.0}
{"source_row": 585, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {684: [(653, 240.0)], 138: [(653, 240.0)], 653: [(363, 0.0)], 363: [], 197: [(684, 5.0), (138, 2.0)]}\nsource = 197\ntarget = 363\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 245.0, "source_answer": 245.0}
{"source_row": 586, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {948: [(330, 0.0)], 805: [(330, 0.0)], 17: [(330, 0.0)], 228: [(805, 60.0), (948, 30.0), (17, 45.0)], 330: [], 861: [(228, 15.0)]}\nsource = 861\ntarget = 330\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 587, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {560: [(172, 2.0), (949, 3.0)], 172: [(957, 0.0)], 949: [(957, 0.0)], 957: [], 552: [(560, 5.0)]}\nsource = 552\ntarget = 957\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 588, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {859: [(762, 5.0)], 570: [(762, 5.0)], 89: [(939, 0.0)], 762: [(89, 10.0)], 939: [], 880: [(859, 5.0), (570, 2.0)]}\nsource = 880\ntarget = 939\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 589, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {109: [(947, 0.0)], 847: [(109, 60.0)], 314: [(109, 60.0)], 947: [], 819: [(847, 120.0), (314, 20160.0)]}\nsource = 819\ntarget = 947\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20220.0, "source_answer": 20220.0}
{"source_row": 590, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {439: [(763, 2.0)], 619: [(763, 2.0)], 763: [(604, 0.0)], 122: [(763, 2.0)], 604: [], 554: [(439, 2.0), (619, 1.0), (122, 3.0)]}\nsource = 554\ntarget = 604\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 591, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {173: [(329, 0.0)], 39: [(329, 0.0)], 922: [(329, 0.0)], 297: [(39, 30.0), (922, 60.0)], 329: [], 289: [(173, 5.0), (297, 15.0)]}\nsource = 289\ntarget = 329\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 592, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {123: [(270, 0.0)], 283: [(270, 0.0)], 631: [(270, 0.0)], 407: [(283, 1051200.0), (123, 259200.0), (631, 518400.0)], 270: [], 339: [(407, 129600.0)]}\nsource = 339\ntarget = 270\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1180800.0, "source_answer": 1180800.0}
{"source_row": 593, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {197: [(484, 1.0)], 451: [(484, 1.0)], 484: [(916, 1.0)], 916: [(329, 0.0)], 329: [], 584: [(197, 1.0), (451, 1.0)]}\nsource = 584\ntarget = 329\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 594, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {888: [(394, 60.0)], 512: [(394, 60.0)], 394: [(876, 0.0)], 876: [], 761: [(888, 30.0), (512, 15.0)]}\nsource = 761\ntarget = 876\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 595, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {477: [(970, 10.0)], 970: [(460, 0.0)], 75: [(460, 0.0)], 813: [(460, 0.0)], 460: [], 852: [(477, 5.0), (75, 15.0), (813, 5.0)]}\nsource = 852\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 596, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {340: [(193, 30.0)], 193: [(930, 0.0)], 558: [(930, 0.0)], 553: [(930, 0.0)], 930: [], 294: [(340, 15.0), (558, 10.0), (553, 10.0)]}\nsource = 294\ntarget = 930\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 597, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {184: [(727, 2.0)], 727: [(856, 1.0)], 856: [(336, 0.0)], 778: [(336, 0.0)], 336: [], 635: [(184, 5.0), (778, 10.0)]}\nsource = 635\ntarget = 336\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 598, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {185: [(540, 1.0)], 904: [(246, 3.0)], 246: [(202, 0.0)], 540: [(246, 3.0)], 202: [], 165: [(185, 1.0), (904, 2.0)]}\nsource = 165\ntarget = 202\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 599, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {910: [(156, 15.0)], 372: [(156, 15.0)], 156: [(146, 0.0)], 146: [], 757: [(910, 5.0), (372, 10.0)]}\nsource = 757\ntarget = 146\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 600, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {736: [(952, 20160.0), (658, 20160.0), (507, 30240.0)], 658: [(366, 0.0)], 507: [(366, 0.0)], 952: [(366, 0.0)], 366: [], 741: [(736, 60.0)]}\nsource = 741\ntarget = 366\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30300.0, "source_answer": 30300.0}
{"source_row": 601, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {666: [(237, 30.0)], 237: [(630, 0.0)], 546: [(630, 0.0)], 874: [(630, 0.0)], 630: [], 859: [(666, 10.0), (546, 1440.0), (874, 10.0)]}\nsource = 859\ntarget = 630\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1440.0, "source_answer": 1440.0}
{"source_row": 602, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {394: [(933, 0.0)], 266: [(485, 2.0)], 485: [(933, 0.0)], 933: [], 969: [(394, 5.0), (266, 10.0)]}\nsource = 969\ntarget = 933\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 603, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {522: [(759, 0.0)], 389: [(759, 0.0)], 716: [(759, 0.0)], 764: [(389, 2.0), (522, 5.0)], 759: [], 586: [(716, 15.0), (764, 1.0)]}\nsource = 586\ntarget = 759\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 604, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {756: [(6, 5.0), (484, 10.0), (197, 10.0)], 484: [(652, 0.0)], 197: [(652, 0.0)], 6: [(652, 0.0)], 652: [], 478: [(756, 5.0)]}\nsource = 478\ntarget = 652\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 605, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {637: [(218, 3.0)], 305: [(218, 3.0)], 218: [(508, 1.0)], 508: [(509, 0.0)], 509: [], 288: [(637, 5.0), (305, 3.0)]}\nsource = 288\ntarget = 509\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 606, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(567, 0.0)], 775: [(544, 90.0), (505, 60.0)], 544: [(567, 0.0)], 567: [], 462: [(775, 20.0)]}\nsource = 462\ntarget = 567\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 110.0, "source_answer": 110.0}
{"source_row": 607, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {600: [(184, 15.0), (644, 60.0)], 184: [(597, 0.0)], 644: [(597, 0.0)], 597: [], 950: [(600, 60.0)]}\nsource = 950\ntarget = 597\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 608, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {876: [(636, 1.0)], 767: [(876, 2.0)], 636: [(647, 0.0)], 913: [(647, 0.0)], 647: [], 944: [(767, 5.0), (913, 10.0)]}\nsource = 944\ntarget = 647\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 609, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {57: [(30, 2.0)], 857: [(30, 2.0)], 30: [(263, 15.0)], 263: [(521, 0.0)], 521: [], 16: [(57, 10.0), (857, 5.0)]}\nsource = 16\ntarget = 521\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 610, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {160: [(221, 2.0)], 25: [(221, 2.0)], 266: [(221, 2.0)], 221: [(589, 0.0)], 589: [], 437: [(160, 5.0), (25, 10.0), (266, 15.0)]}\nsource = 437\ntarget = 589\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 611, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {737: [(657, 0.0)], 377: [(930, 45.0), (737, 30.0)], 930: [(657, 0.0)], 657: [], 599: [(377, 60.0)]}\nsource = 599\ntarget = 657\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 612, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {842: [(891, 15.0)], 891: [(609, 0.0)], 409: [(609, 0.0)], 609: [], 313: [(842, 10.0), (409, 15.0)]}\nsource = 313\ntarget = 609\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 613, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {50: [(414, 0.0)], 155: [(17, 60480.0), (50, 129600.0)], 17: [(414, 0.0)], 414: [], 290: [(155, 30240.0)]}\nsource = 290\ntarget = 414\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 159840.0, "source_answer": 159840.0}
{"source_row": 614, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {792: [(773, 7884000.0)], 372: [(773, 7884000.0)], 187: [(773, 7884000.0)], 773: [(133, 0.0)], 133: [], 707: [(792, 5256000.0), (372, 5256000.0), (187, 1051200.0)]}\nsource = 707\ntarget = 133\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13140000.0, "source_answer": 13140000.0}
{"source_row": 615, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {988: [(754, 259200.0)], 754: [(783, 0.0)], 43: [(754, 259200.0)], 783: [], 822: [(988, 4204800.0), (43, 86400.0)]}\nsource = 822\ntarget = 783\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4464000.0, "source_answer": 4464000.0}
{"source_row": 616, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {709: [(796, 129600.0), (598, 20160.0), (394, 259200.0)], 598: [(212, 0.0)], 394: [(212, 0.0)], 796: [(212, 0.0)], 212: [], 980: [(709, 40320.0)]}\nsource = 980\ntarget = 212\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 299520.0, "source_answer": 299520.0}
{"source_row": 617, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {754: [(588, 0.0)], 25: [(165, 180.0)], 165: [(588, 0.0)], 588: [], 628: [(754, 60.0), (25, 120.0)]}\nsource = 628\ntarget = 588\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 618, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(136, 5.0), (227, 5.0), (88, 10.0)], 227: [(659, 0.0)], 88: [(659, 0.0)], 136: [(659, 0.0)], 659: [], 552: [(458, 15.0)]}\nsource = 552\ntarget = 659\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 619, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {246: [(928, 1.0)], 430: [(928, 1.0)], 928: [(298, 0.0)], 265: [(298, 0.0)], 298: [], 855: [(246, 5.0), (430, 2.0), (265, 1.0)]}\nsource = 855\ntarget = 298\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 620, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {790: [(940, 5.0)], 363: [(940, 5.0)], 940: [(186, 2.0)], 186: [(928, 0.0)], 928: [], 209: [(790, 10.0), (363, 2.0)]}\nsource = 209\ntarget = 928\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 621, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {886: [(246, 120.0)], 793: [(659, 0.0)], 246: [(659, 0.0)], 659: [], 113: [(886, 120.0), (793, 20160.0)]}\nsource = 113\ntarget = 659\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 622, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {335: [(959, 2.0)], 28: [(959, 2.0)], 959: [(82, 1.0)], 82: [(584, 0.0)], 584: [], 870: [(335, 5.0), (28, 10.0)]}\nsource = 870\ntarget = 584\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 623, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {320: [(302, 0.0)], 60: [(320, 30.0)], 398: [(302, 0.0)], 670: [(320, 30.0)], 302: [], 751: [(60, 15.0), (398, 45.0), (670, 20.0)]}\nsource = 751\ntarget = 302\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 624, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {698: [(813, 0.0)], 609: [(813, 0.0)], 934: [(813, 0.0)], 790: [(609, 15.0), (698, 30.0), (934, 60.0)], 813: [], 760: [(790, 30.0)]}\nsource = 760\ntarget = 813\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 625, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {376: [(402, 10.0)], 402: [(372, 20.0)], 339: [(372, 20.0)], 372: [(918, 0.0)], 918: [], 39: [(376, 5.0), (339, 15.0)]}\nsource = 39\ntarget = 918\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 626, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {511: [(584, 15.0)], 590: [(584, 15.0)], 584: [(514, 0.0)], 514: [], 77: [(511, 5.0), (590, 10.0)]}\nsource = 77\ntarget = 514\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 627, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {773: [(447, 60.0), (369, 120.0)], 369: [(621, 0.0)], 190: [(621, 0.0)], 447: [(621, 0.0)], 621: [], 186: [(773, 120.0), (190, 30.0)]}\nsource = 186\ntarget = 621\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 628, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {608: [(8, 30.0)], 8: [(17, 60.0)], 17: [(218, 0.0)], 80: [(218, 0.0)], 218: [], 692: [(608, 5.0), (80, 15.0)]}\nsource = 692\ntarget = 218\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.0, "source_answer": 95.0}
{"source_row": 629, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {874: [(336, 1.0), (540, 1.0), (10, 1.0)], 540: [(274, 0.0)], 10: [(274, 0.0)], 336: [(274, 0.0)], 274: [], 992: [(874, 5.0)]}\nsource = 992\ntarget = 274\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 630, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {706: [(185, 0.0)], 517: [(76, 20.0), (706, 30.0)], 76: [(185, 0.0)], 185: [], 959: [(517, 15.0)]}\nsource = 959\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 631, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {146: [(440, 2.0)], 338: [(440, 2.0)], 180: [(440, 2.0)], 440: [(515, 0.0)], 515: [], 526: [(146, 1.0), (338, 2.0), (180, 1.0)]}\nsource = 526\ntarget = 515\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 632, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {759: [(786, 1440.0)], 701: [(786, 1440.0)], 786: [(63, 1440.0)], 63: [(783, 0.0)], 783: [], 88: [(759, 525600.0), (701, 525600.0)]}\nsource = 88\ntarget = 783\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 528480.0, "source_answer": 528480.0}
{"source_row": 633, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {686: [(21, 120.0)], 21: [(316, 20160.0), (700, 1440.0)], 700: [(452, 0.0)], 316: [(452, 0.0)], 452: [], 727: [(686, 30.0)]}\nsource = 727\ntarget = 452\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20310.0, "source_answer": 20310.0}
{"source_row": 634, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {957: [(736, 30.0), (26, 10.0)], 736: [(416, 0.0)], 26: [(416, 0.0)], 932: [(416, 0.0)], 416: [], 126: [(957, 1.0), (932, 30.0)]}\nsource = 126\ntarget = 416\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0}
{"source_row": 635, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {433: [(987, 10080.0), (502, 10080.0)], 987: [(147, 0.0)], 502: [(147, 0.0)], 147: [], 461: [(433, 60.0)]}\nsource = 461\ntarget = 147\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10140.0, "source_answer": 10140.0}
{"source_row": 636, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {542: [(6, 0.0)], 868: [(6, 0.0)], 251: [(6, 0.0)], 679: [(868, 2.0), (542, 2.0), (251, 1.0)], 6: [], 306: [(679, 5.0)]}\nsource = 306\ntarget = 6\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 637, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {19: [(242, 15.0)], 680: [(242, 15.0)], 969: [(242, 15.0)], 242: [(396, 0.0)], 396: [], 466: [(19, 10.0), (680, 10.0), (969, 5.0)]}\nsource = 466\ntarget = 396\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 638, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {731: [(790, 0.0)], 124: [(790, 0.0)], 875: [(790, 0.0)], 819: [(124, 10080.0), (731, 10080.0), (875, 10080.0)], 790: [], 346: [(819, 10080.0)]}\nsource = 346\ntarget = 790\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 639, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {727: [(54, 30.0)], 614: [(54, 30.0)], 337: [(54, 30.0)], 54: [(676, 0.0)], 676: [], 47: [(727, 5.0), (614, 5.0), (337, 10.0)]}\nsource = 47\ntarget = 676\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 640, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {988: [(78, 20.0)], 894: [(78, 20.0)], 78: [(993, 45.0)], 993: [(45, 0.0)], 45: [], 296: [(988, 30.0), (894, 10.0)]}\nsource = 296\ntarget = 45\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 95.0, "source_answer": 95.0}
{"source_row": 641, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {164: [(33, 388800.0)], 33: [(41, 0.0)], 783: [(41, 0.0)], 397: [(41, 0.0)], 41: [], 955: [(164, 60.0), (783, 60.0), (397, 60.0)]}\nsource = 955\ntarget = 41\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 388860.0, "source_answer": 388860.0}
{"source_row": 642, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {695: [(758, 30.0)], 666: [(758, 30.0)], 758: [(743, 0.0)], 702: [(758, 30.0)], 743: [], 611: [(695, 30.0), (666, 60.0), (702, 120.0)]}\nsource = 611\ntarget = 743\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 643, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {529: [(817, 10.0)], 525: [(817, 10.0)], 817: [(847, 3.0)], 847: [(991, 0.0)], 991: [], 612: [(529, 5.0), (525, 2.0)]}\nsource = 612\ntarget = 991\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 644, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {460: [(924, 8.0)], 619: [(924, 8.0)], 89: [(924, 8.0)], 924: [(23, 0.0)], 23: [], 818: [(460, 5.0), (619, 10.0), (89, 2.0)]}\nsource = 818\ntarget = 23\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 645, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {318: [(267, 0.0)], 459: [(285, 15.0)], 285: [(267, 0.0)], 267: [], 84: [(318, 10.0), (459, 20.0)]}\nsource = 84\ntarget = 267\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 646, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {637: [(696, 3.0)], 462: [(839, 0.0)], 23: [(696, 3.0)], 696: [(839, 0.0)], 839: [], 394: [(637, 12.0), (462, 3.0), (23, 10.0)]}\nsource = 394\ntarget = 839\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 647, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {54: [(662, 0.0)], 296: [(24, 120.0)], 24: [(662, 0.0)], 976: [(296, 1.0)], 662: [], 132: [(54, 1.0), (976, 0.17)]}\nsource = 132\ntarget = 662\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 121.17, "source_answer": 121.17}
{"source_row": 648, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {818: [(193, 60.0)], 193: [(334, 0.0)], 939: [(334, 0.0)], 49: [(334, 0.0)], 334: [], 408: [(818, 60.0), (939, 15.0), (49, 120.0)]}\nsource = 408\ntarget = 334\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 649, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {852: [(581, 1.0)], 581: [(837, 1.0)], 584: [(837, 1.0)], 837: [(963, 0.0)], 963: [], 767: [(852, 2.0), (584, 1.0)]}\nsource = 767\ntarget = 963\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 650, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {249: [(129, 30.0), (398, 30.0)], 129: [(784, 0.0)], 398: [(784, 0.0)], 784: [], 503: [(249, 10.0)]}\nsource = 503\ntarget = 784\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 651, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {643: [(913, 10.0)], 93: [(913, 10.0)], 913: [(809, 2.0)], 809: [(474, 0.0)], 474: [], 788: [(643, 5.0), (93, 2.0)]}\nsource = 788\ntarget = 474\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 652, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {185: [(40, 40320.0), (206, 10080.0), (216, 4320.0)], 206: [(709, 0.0)], 216: [(709, 0.0)], 40: [(709, 0.0)], 709: [], 4: [(185, 40320.0)]}\nsource = 4\ntarget = 709\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80640.0, "source_answer": 80640.0}
{"source_row": 653, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {236: [(970, 10.0)], 835: [(986, 20.0)], 970: [(986, 20.0)], 986: [(617, 0.0)], 617: [], 634: [(236, 30.0), (835, 10.0)]}\nsource = 634\ntarget = 617\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 654, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {987: [(767, 45.0)], 33: [(767, 45.0)], 767: [(591, 5.0)], 591: [(463, 0.0)], 463: [], 39: [(987, 30.0), (33, 15.0)]}\nsource = 39\ntarget = 463\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0}
{"source_row": 655, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {941: [(462, 1.0), (617, 1.0), (891, 1.0)], 617: [(447, 0.0)], 891: [(447, 0.0)], 462: [(447, 0.0)], 447: [], 199: [(941, 2.0)]}\nsource = 199\ntarget = 447\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 656, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {75: [(618, 1.0), (525, 1.0), (164, 1.0)], 525: [(143, 0.0)], 164: [(143, 0.0)], 618: [(143, 0.0)], 143: [], 690: [(75, 3.0)]}\nsource = 690\ntarget = 143\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 657, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {825: [(924, 60.0)], 776: [(924, 60.0)], 144: [(924, 60.0)], 924: [(434, 0.0)], 434: [], 401: [(825, 360.0), (776, 180.0), (144, 300.0)]}\nsource = 401\ntarget = 434\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 420.0, "source_answer": 420.0}
{"source_row": 658, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {32: [(811, 20160.0)], 811: [(913, 40320.0), (851, 40320.0)], 851: [(758, 0.0)], 913: [(758, 0.0)], 758: [], 392: [(32, 1440.0)]}\nsource = 392\ntarget = 758\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 61920.0, "source_answer": 61920.0}
{"source_row": 659, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {531: [(477, 20160.0)], 601: [(55, 0.0)], 477: [(55, 0.0)], 305: [(55, 0.0)], 55: [], 424: [(531, 43200.0), (601, 86400.0), (305, 20160.0)]}\nsource = 424\ntarget = 55\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86400.0, "source_answer": 86400.0}
{"source_row": 660, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(5, 10080.0)], 5: [(245, 0.0)], 885: [(245, 0.0)], 245: [], 116: [(963, 20160.0), (885, 30240.0)]}\nsource = 116\ntarget = 245\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30240.0, "source_answer": 30240.0}
{"source_row": 661, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {279: [(53, 0.0)], 101: [(279, 5.0)], 954: [(53, 0.0)], 558: [(53, 0.0)], 53: [], 560: [(101, 3.0), (954, 7.0), (558, 4.0)]}\nsource = 560\ntarget = 53\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 662, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {44: [(207, 20.0)], 207: [(86, 15.0)], 86: [(615, 0.0)], 868: [(207, 20.0)], 615: [], 313: [(44, 10.0), (868, 5.0)]}\nsource = 313\ntarget = 615\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 663, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {782: [(564, 5.0)], 462: [(564, 5.0)], 618: [(564, 5.0)], 564: [(23, 0.0)], 23: [], 869: [(782, 30.0), (462, 20.0), (618, 10.0)]}\nsource = 869\ntarget = 23\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 664, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {766: [(660, 2.0)], 660: [(186, 1.0), (250, 2.0)], 250: [(633, 0.0)], 186: [(633, 0.0)], 633: [], 89: [(766, 5.0)]}\nsource = 89\ntarget = 633\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 665, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {999: [(144, 30240.0), (128, 80640.0)], 144: [(680, 0.0)], 128: [(680, 0.0)], 680: [], 929: [(999, 60.0)]}\nsource = 929\ntarget = 680\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80700.0, "source_answer": 80700.0}
{"source_row": 666, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {22: [(27, 2.0)], 27: [(448, 10.0)], 448: [(308, 0.0)], 246: [(27, 2.0)], 308: [], 609: [(22, 1.0), (246, 3.0)]}\nsource = 609\ntarget = 308\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 667, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {880: [(913, 0.0)], 82: [(880, 43200.0)], 826: [(913, 0.0)], 454: [(913, 0.0)], 913: [], 508: [(82, 10080.0), (826, 2880.0), (454, 43200.0)]}\nsource = 508\ntarget = 913\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 53280.0, "source_answer": 53280.0}
{"source_row": 668, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {453: [(535, 20.0)], 254: [(596, 2.0)], 596: [(535, 20.0)], 535: [(748, 0.0)], 748: [], 210: [(453, 5.0), (254, 5.0)]}\nsource = 210\ntarget = 748\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 669, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {552: [(237, 15.0)], 442: [(237, 15.0)], 237: [(618, 5.0)], 618: [(281, 0.0)], 281: [], 288: [(552, 10.0), (442, 10.0)]}\nsource = 288\ntarget = 281\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 670, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {771: [(692, 2.0)], 610: [(692, 2.0)], 692: [(839, 1.0)], 839: [(145, 0.0)], 145: [], 544: [(771, 5.0), (610, 3.0)]}\nsource = 544\ntarget = 145\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 671, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {175: [(230, 5.0), (73, 5.0), (18, 10.0)], 73: [(236, 0.0)], 18: [(236, 0.0)], 230: [(236, 0.0)], 236: [], 810: [(175, 30.0)]}\nsource = 810\ntarget = 236\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 672, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {815: [(347, 60.0)], 313: [(347, 60.0)], 350: [(347, 60.0)], 347: [(51, 0.0)], 51: [], 219: [(815, 30.0), (313, 15.0), (350, 10.0)]}\nsource = 219\ntarget = 51\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 673, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {950: [(530, 2.0)], 530: [(44, 0.0)], 149: [(44, 0.0)], 914: [(44, 0.0)], 44: [], 538: [(950, 5.0), (149, 5.0), (914, 2.0)]}\nsource = 538\ntarget = 44\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 674, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {512: [(286, 0.5)], 695: [(512, 1.0)], 286: [(892, 0.0)], 623: [(892, 0.0)], 892: [], 792: [(695, 0.17), (623, 1.0)]}\nsource = 792\ntarget = 892\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.67, "source_answer": 1.67}
{"source_row": 675, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {947: [(333, 0.0)], 592: [(333, 0.0)], 894: [(333, 0.0)], 864: [(592, 10.0)], 333: [], 346: [(947, 5.0), (894, 2.0), (864, 5.0)]}\nsource = 346\ntarget = 333\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 676, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {391: [(457, 0.0)], 541: [(444, 1.0)], 444: [(829, 1.0)], 829: [(457, 0.0)], 457: [], 238: [(391, 2.0), (541, 1.0)]}\nsource = 238\ntarget = 457\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 677, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {758: [(62, 2.0)], 62: [(9, 10.0)], 889: [(62, 2.0)], 9: [(150, 0.0)], 150: [], 564: [(758, 5.0), (889, 5.0)]}\nsource = 564\ntarget = 150\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 678, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {309: [(903, 15.0)], 181: [(493, 2.0), (903, 15.0)], 903: [(487, 0.0)], 493: [(487, 0.0)], 487: [], 776: [(309, 5.0), (181, 10.0)]}\nsource = 776\ntarget = 487\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 679, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {372: [(722, 60.0), (524, 60.0), (466, 45.0)], 524: [(676, 0.0)], 466: [(676, 0.0)], 722: [(676, 0.0)], 676: [], 505: [(372, 30.0)]}\nsource = 505\ntarget = 676\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 680, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {44: [(842, 5.0)], 8: [(244, 1.0)], 842: [(244, 1.0)], 244: [(721, 0.0)], 721: [], 89: [(44, 3.0), (8, 3.0)]}\nsource = 89\ntarget = 721\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 681, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {879: [(960, 0.0)], 431: [(879, 60.0)], 624: [(960, 0.0)], 144: [(960, 0.0)], 960: [], 785: [(431, 20160.0), (624, 60.0), (144, 120.0)]}\nsource = 785\ntarget = 960\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20220.0, "source_answer": 20220.0}
{"source_row": 682, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {669: [(262, 5.0)], 196: [(669, 5.0)], 262: [(656, 0.0)], 759: [(656, 0.0)], 656: [], 500: [(196, 10.0), (759, 20.0)]}\nsource = 500\ntarget = 656\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 683, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {344: [(894, 30240.0)], 90: [(894, 30240.0)], 701: [(894, 30240.0)], 894: [(317, 0.0)], 317: [], 450: [(344, 10080.0), (90, 20160.0), (701, 10080.0)]}\nsource = 450\ntarget = 317\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50400.0, "source_answer": 50400.0}
{"source_row": 684, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {935: [(960, 5.0)], 793: [(960, 5.0)], 960: [(450, 0.0)], 450: [], 122: [(935, 15.0), (793, 10.0)]}\nsource = 122\ntarget = 450\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 685, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {806: [(326, 5.0)], 326: [(135, 0.0)], 117: [(135, 0.0)], 135: [], 519: [(806, 1.0), (117, 10.0)]}\nsource = 519\ntarget = 135\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 686, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {168: [(919, 2.0)], 919: [(699, 480.0)], 699: [(19, 0.0)], 700: [(19, 0.0)], 19: [], 222: [(168, 5.0), (700, 172800.0)]}\nsource = 222\ntarget = 19\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 172800.0, "source_answer": 172800.0}
{"source_row": 687, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {845: [(853, 20.0), (122, 10.0), (862, 15.0)], 122: [(802, 0.0)], 862: [(802, 0.0)], 853: [(802, 0.0)], 802: [], 770: [(845, 5.0)]}\nsource = 770\ntarget = 802\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 688, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {739: [(480, 0.0)], 335: [(243, 15.0)], 243: [(480, 0.0)], 480: [], 632: [(739, 5.0), (335, 10.0)]}\nsource = 632\ntarget = 480\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 689, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {193: [(446, 5.0)], 341: [(446, 5.0)], 446: [(587, 10.0)], 587: [(56, 0.0)], 56: [], 885: [(193, 5.0), (341, 5.0)]}\nsource = 885\ntarget = 56\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 690, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {249: [(410, 0.0)], 272: [(249, 5.0)], 989: [(410, 0.0)], 149: [(272, 10.0)], 410: [], 526: [(989, 10080.0), (149, 15.0)]}\nsource = 526\ntarget = 410\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10080.0, "source_answer": 10080.0}
{"source_row": 691, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {520: [(317, 10.0)], 317: [(76, 20.0), (134, 15.0)], 134: [(112, 0.0)], 76: [(112, 0.0)], 112: [], 960: [(520, 5.0)]}\nsource = 960\ntarget = 112\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 692, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {58: [(852, 10.0)], 852: [(327, 15.0)], 327: [(97, 0.0)], 766: [(97, 0.0)], 97: [], 103: [(58, 5.0), (766, 5.0)]}\nsource = 103\ntarget = 97\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 693, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {410: [(194, 0.0)], 691: [(778, 3.0)], 778: [(194, 0.0)], 194: [], 521: [(410, 1.0), (691, 2.0)]}\nsource = 521\ntarget = 194\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 694, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {410: [(931, 0.0)], 631: [(632, 3.0)], 632: [(859, 4.0)], 859: [(931, 0.0)], 931: [], 811: [(410, 2.0), (631, 2.0)]}\nsource = 811\ntarget = 931\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 695, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {526: [(233, 10.0)], 233: [(403, 60.0)], 403: [(63, 0.0)], 585: [(403, 60.0)], 63: [], 680: [(526, 30.0), (585, 240.0)]}\nsource = 680\ntarget = 63\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 696, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {105: [(913, 10.0), (557, 2.0), (761, 10.0)], 557: [(84, 0.0)], 761: [(84, 0.0)], 913: [(84, 0.0)], 84: [], 462: [(105, 5.0)]}\nsource = 462\ntarget = 84\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 697, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {247: [(969, 2.0)], 591: [(969, 2.0)], 969: [(626, 0.0)], 626: [], 167: [(247, 5.0), (591, 5.0)]}\nsource = 167\ntarget = 626\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 698, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {149: [(447, 10.0)], 139: [(447, 10.0)], 447: [(452, 0.0)], 452: [], 734: [(149, 5.0), (139, 5.0)]}\nsource = 734\ntarget = 452\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 699, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {582: [(811, 2.0), (141, 2.0), (744, 2.0)], 141: [(507, 0.0)], 744: [(507, 0.0)], 811: [(507, 0.0)], 507: [], 492: [(582, 5.0)]}\nsource = 492\ntarget = 507\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 700, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {535: [(913, 3.0)], 915: [(913, 3.0)], 913: [(20, 0.0)], 288: [(20, 0.0)], 20: [], 976: [(535, 5.0), (915, 2.0), (288, 10.0)]}\nsource = 976\ntarget = 20\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 701, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {655: [(98, 120.0), (715, 60.0), (256, 20160.0)], 715: [(112, 0.0)], 256: [(112, 0.0)], 98: [(112, 0.0)], 112: [], 106: [(655, 30.0)]}\nsource = 106\ntarget = 112\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20190.0, "source_answer": 20190.0}
{"source_row": 702, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {501: [(255, 20160.0)], 636: [(536, 0.0)], 255: [(536, 0.0)], 536: [], 152: [(501, 20160.0), (636, 120960.0)]}\nsource = 152\ntarget = 536\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120960.0, "source_answer": 120960.0}
{"source_row": 703, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {3: [(401, 15.0)], 0: [(401, 15.0)], 401: [(161, 15.0)], 161: [(772, 0.0)], 772: [], 501: [(3, 5.0), (0, 10.0)]}\nsource = 501\ntarget = 772\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 704, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {694: [(767, 0.0)], 242: [(587, 10080.0)], 622: [(568, 14400.0)], 587: [(767, 0.0)], 568: [(767, 0.0)], 767: [], 473: [(694, 20160.0), (242, 4320.0), (622, 4320.0)]}\nsource = 473\ntarget = 767\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 705, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {554: [(721, 5.0)], 721: [(380, 0.0)], 398: [(380, 0.0)], 380: [], 89: [(554, 5.0), (398, 10.0)]}\nsource = 89\ntarget = 380\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 706, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {738: [(413, 30.0)], 413: [(515, 0.0)], 916: [(515, 0.0)], 31: [(515, 0.0)], 515: [], 90: [(738, 10.0), (916, 20.0), (31, 20.0)]}\nsource = 90\ntarget = 515\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 707, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {449: [(527, 2.0)], 527: [(224, 15.0)], 338: [(224, 15.0)], 224: [(786, 10.0)], 786: [(842, 0.0)], 842: [], 629: [(449, 5.0), (338, 2.0)]}\nsource = 629\ntarget = 842\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 708, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {194: [(612, 0.0)], 756: [(960, 60.0), (483, 30.0)], 483: [(612, 0.0)], 960: [(612, 0.0)], 886: [(612, 0.0)], 612: [], 754: [(194, 5.0), (756, 1440.0), (886, 10080.0)]}\nsource = 754\ntarget = 612\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10080.0, "source_answer": 10080.0}
{"source_row": 709, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {883: [(298, 0.0)], 333: [(349, 120.0)], 349: [(298, 0.0)], 750: [(298, 0.0)], 622: [(298, 0.0)], 298: [], 992: [(883, 30.0), (333, 120.0), (750, 45.0), (622, 60.0)]}\nsource = 992\ntarget = 298\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 710, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {190: [(967, 0.0)], 943: [(190, 30.0)], 712: [(967, 0.0)], 395: [(967, 0.0)], 967: [], 396: [(943, 15.0), (712, 5.0), (395, 30.0)]}\nsource = 396\ntarget = 967\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 711, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {105: [(847, 60.0)], 847: [(486, 2.0)], 486: [(84, 180.0)], 87: [(710, 0.0)], 84: [(710, 0.0)], 710: [], 877: [(105, 5.0), (87, 1.0)]}\nsource = 877\ntarget = 710\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 247.0, "source_answer": 247.0}
{"source_row": 712, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {675: [(995, 40320.0)], 65: [(96, 0.0)], 551: [(995, 40320.0)], 995: [(96, 0.0)], 96: [], 958: [(675, 129600.0), (65, 20160.0), (551, 40320.0)]}\nsource = 958\ntarget = 96\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 169920.0, "source_answer": 169920.0}
{"source_row": 713, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {675: [(850, 60.0)], 850: [(631, 0.0)], 974: [(631, 0.0)], 631: [], 861: [(675, 120.0), (974, 1440.0)]}\nsource = 861\ntarget = 631\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1440.0, "source_answer": 1440.0}
{"source_row": 714, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {7: [(107, 0.0)], 937: [(107, 0.0)], 900: [(937, 300.0), (7, 120.0)], 107: [], 676: [(900, 30.0)]}\nsource = 676\ntarget = 107\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 330.0, "source_answer": 330.0}
{"source_row": 715, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {667: [(87, 3.0)], 87: [(301, 2.0), (716, 2.0)], 716: [(408, 1.0)], 301: [(408, 1.0)], 408: [(938, 0.0)], 938: [], 615: [(667, 5.0)]}\nsource = 615\ntarget = 938\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 716, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {403: [(857, 10.0)], 857: [(715, 15.0)], 387: [(715, 15.0)], 715: [(557, 10.0)], 557: [(846, 0.0)], 846: [], 661: [(403, 5.0), (387, 5.0)]}\nsource = 661\ntarget = 846\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 717, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {640: [(363, 5.0)], 78: [(640, 5.0)], 801: [(363, 5.0)], 363: [(131, 3.0)], 131: [(441, 0.0)], 441: [], 543: [(78, 2.0), (801, 2.0)]}\nsource = 543\ntarget = 441\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 718, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {345: [(876, 0.0)], 286: [(876, 0.0)], 401: [(876, 0.0)], 579: [(876, 0.0)], 217: [(345, 15.0)], 876: [], 3: [(286, 5.0), (401, 15.0), (579, 20.0), (217, 15.0)]}\nsource = 3\ntarget = 876\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 719, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {500: [(893, 2.0)], 160: [(893, 2.0)], 893: [(630, 1.0)], 630: [(459, 0.0)], 459: [], 385: [(500, 2.0), (160, 0.5)]}\nsource = 385\ntarget = 459\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 720, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {134: [(171, 0.0)], 383: [(134, 15.0)], 15: [(134, 15.0)], 171: [], 7: [(383, 15.0), (15, 20.0)]}\nsource = 7\ntarget = 171\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 721, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {826: [(401, 5.0)], 201: [(401, 5.0)], 504: [(814, 7.0)], 401: [(814, 7.0)], 814: [(605, 0.0)], 605: [], 418: [(826, 10.0), (201, 5.0), (504, 10.0)]}\nsource = 418\ntarget = 605\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 722, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {43: [(762, 10.0)], 762: [(86, 0.0)], 729: [(86, 0.0)], 632: [(86, 0.0)], 647: [(86, 0.0)], 86: [], 485: [(43, 10.0), (729, 15.0), (632, 5.0), (647, 10.0)]}\nsource = 485\ntarget = 86\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 723, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {305: [(772, 20.0)], 476: [(772, 20.0)], 772: [(290, 0.0)], 290: [], 627: [(305, 60.0), (476, 30.0)]}\nsource = 627\ntarget = 290\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0}
{"source_row": 724, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {970: [(446, 10.0)], 787: [(533, 10.0)], 533: [(446, 10.0)], 446: [(968, 15.0)], 968: [(824, 0.0)], 824: [], 367: [(970, 5.0), (787, 2.0)]}\nsource = 367\ntarget = 824\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 725, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {484: [(310, 10.0)], 949: [(310, 10.0)], 664: [(310, 10.0)], 310: [(58, 2.0)], 58: [(346, 0.0)], 346: [], 625: [(484, 20.0), (949, 3.0), (664, 5.0)]}\nsource = 625\ntarget = 346\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 726, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {278: [(171, 2.0)], 642: [(539, 1.0)], 539: [(171, 2.0)], 171: [(929, 1.0)], 929: [(305, 0.0)], 305: [], 723: [(278, 5.0), (642, 2.0)]}\nsource = 723\ntarget = 305\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 727, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {195: [(500, 5.0), (123, 2.0)], 500: [(540, 0.0)], 123: [(540, 0.0)], 540: [], 574: [(195, 30.0)]}\nsource = 574\ntarget = 540\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 728, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {506: [(664, 10.0)], 664: [(23, 15.0)], 23: [(789, 0.0)], 422: [(226, 5.0)], 226: [(789, 0.0)], 789: [], 904: [(506, 5.0), (422, 10.0)]}\nsource = 904\ntarget = 789\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 729, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {196: [(697, 5.0)], 697: [(650, 3.0)], 321: [(650, 3.0)], 650: [(159, 1.0)], 159: [(702, 0.0)], 702: [], 991: [(196, 3.0), (321, 3.0)]}\nsource = 991\ntarget = 702\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 730, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(791, 0.5)], 497: [(329, 1.0), (748, 1.0)], 748: [(791, 0.5)], 329: [(791, 0.5)], 791: [(428, 0.0)], 428: [], 759: [(316, 5.0), (497, 3.0)]}\nsource = 759\ntarget = 428\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.5, "source_answer": 5.5}
{"source_row": 731, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {213: [(97, 10.0)], 97: [(686, 15.0)], 686: [(788, 20.0)], 948: [(788, 20.0)], 788: [(973, 0.0)], 973: [], 162: [(213, 5.0), (948, 5.0)]}\nsource = 162\ntarget = 973\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 732, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {40: [(201, 2.0)], 12: [(201, 2.0)], 925: [(201, 2.0)], 337: [(201, 2.0)], 201: [(157, 0.0)], 157: [], 549: [(40, 5.0), (12, 3.0), (925, 2.0), (337, 2.0)]}\nsource = 549\ntarget = 157\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 733, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {729: [(123, 2.0)], 977: [(123, 2.0)], 538: [(123, 2.0)], 123: [(440, 10.0)], 440: [(307, 0.0)], 307: [], 714: [(729, 5.0), (977, 3.0), (538, 3.0)]}\nsource = 714\ntarget = 307\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 734, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {278: [(663, 40.0)], 788: [(448, 5.0)], 448: [(663, 40.0)], 663: [(458, 0.0)], 458: [], 706: [(278, 10.0), (788, 15.0)]}\nsource = 706\ntarget = 458\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 735, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {73: [(619, 120.0), (799, 180.0)], 619: [(420, 0.0)], 799: [(420, 0.0)], 909: [(420, 0.0)], 214: [(420, 0.0)], 420: [], 941: [(73, 60.0), (909, 60.0), (214, 30.0)]}\nsource = 941\ntarget = 420\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 736, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {613: [(553, 5.0)], 753: [(662, 45.0)], 553: [(200, 5.0)], 662: [(553, 5.0)], 200: [(892, 0.0)], 892: [], 946: [(613, 5.0), (753, 10.0)]}\nsource = 946\ntarget = 892\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 737, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {698: [(598, 10.0)], 598: [(21, 5.0)], 558: [(21, 5.0)], 21: [(703, 1.0)], 703: [(884, 0.0)], 884: [], 45: [(698, 5.0), (558, 2.0)]}\nsource = 45\ntarget = 884\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 738, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {619: [(154, 0.0)], 948: [(619, 30240.0)], 390: [(154, 0.0)], 749: [(154, 0.0)], 154: [], 532: [(948, 60480.0), (390, 70560.0), (749, 129600.0)]}\nsource = 532\ntarget = 154\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129600.0, "source_answer": 129600.0}
{"source_row": 739, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {453: [(388, 2.0)], 388: [(904, 5.0)], 160: [(904, 5.0)], 904: [(360, 10.0)], 360: [(661, 0.0)], 661: [], 761: [(453, 5.0), (160, 1.0)]}\nsource = 761\ntarget = 661\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 740, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {591: [(109, 0.0)], 284: [(735, 129600.0)], 735: [(109, 0.0)], 109: [], 99: [(591, 20160.0), (284, 20160.0)]}\nsource = 99\ntarget = 109\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 149760.0, "source_answer": 149760.0}
{"source_row": 741, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {813: [(540, 2.0)], 540: [(469, 2.0)], 469: [(912, 0.0)], 699: [(761, 3.0)], 761: [(912, 0.0)], 912: [], 864: [(813, 2.0), (699, 1.0)]}\nsource = 864\ntarget = 912\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 742, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {617: [(420, 15.0)], 420: [(790, 30.0)], 389: [(832, 60.0)], 832: [(790, 30.0)], 790: [(408, 0.0)], 408: [], 366: [(617, 5.0), (389, 30.0)]}\nsource = 366\ntarget = 408\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 743, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {971: [(268, 10.0)], 269: [(697, 0.0)], 268: [(527, 2.0)], 527: [(356, 5.0)], 356: [(697, 0.0)], 697: [], 990: [(971, 5.0), (269, 2.0)]}\nsource = 990\ntarget = 697\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 744, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {16: [(473, 0.0)], 438: [(16, 120.0)], 104: [(16, 120.0)], 473: [], 447: [(438, 2.0), (104, 5.0)]}\nsource = 447\ntarget = 473\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 125.0, "source_answer": 125.0}
{"source_row": 745, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {769: [(12, 0.0)], 885: [(769, 40320.0)], 615: [(769, 40320.0)], 12: [], 354: [(885, 20160.0), (615, 30240.0)]}\nsource = 354\ntarget = 12\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70560.0, "source_answer": 70560.0}
{"source_row": 746, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {89: [(589, 0.17), (283, 0.17)], 589: [(901, 0.5)], 283: [(901, 0.5)], 901: [(549, 1.0)], 549: [(976, 0.0)], 976: [], 741: [(89, 1.0)]}\nsource = 741\ntarget = 976\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.67, "source_answer": 2.67}
{"source_row": 747, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {824: [(530, 2.0)], 530: [(636, 1.0)], 636: [(592, 1.0)], 145: [(636, 1.0)], 592: [(738, 0.0)], 738: [], 371: [(824, 5.0), (145, 240.0)]}\nsource = 371\ntarget = 738\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 242.0, "source_answer": 242.0}
{"source_row": 748, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {491: [(412, 0.0)], 617: [(199, 30.0)], 199: [(412, 0.0)], 412: [], 595: [(491, 10.0), (617, 15.0)]}\nsource = 595\ntarget = 412\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 749, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {269: [(514, 1.0)], 192: [(514, 1.0)], 514: [(271, 1.0)], 271: [(675, 1.0)], 675: [(184, 0.0)], 184: [], 291: [(269, 1.0), (192, 1.0)]}\nsource = 291\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 750, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {949: [(624, 120.0), (745, 30.0), (937, 120.0)], 745: [(811, 0.0)], 937: [(811, 0.0)], 624: [(811, 0.0)], 811: [], 986: [(949, 120.0)]}\nsource = 986\ntarget = 811\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 751, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {809: [(483, 4320.0)], 521: [(483, 4320.0)], 123: [(444, 0.0)], 483: [(123, 2880.0)], 444: [], 497: [(809, 10080.0), (521, 20160.0)]}\nsource = 497\ntarget = 444\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27360.0, "source_answer": 27360.0}
{"source_row": 752, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {826: [(240, 0.0)], 134: [(240, 0.0)], 974: [(826, 5.0), (134, 5.0)], 238: [(240, 0.0)], 685: [(240, 0.0)], 240: [], 991: [(974, 10.0), (238, 5.0), (685, 5.0)]}\nsource = 991\ntarget = 240\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 753, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {197: [(729, 2628000.0)], 633: [(727, 0.0)], 729: [(727, 0.0)], 727: [], 761: [(197, 518400.0), (633, 1051200.0)]}\nsource = 761\ntarget = 727\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3146400.0, "source_answer": 3146400.0}
{"source_row": 754, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {640: [(52, 15.0)], 528: [(640, 30.0)], 52: [(796, 0.0)], 441: [(796, 0.0)], 796: [], 29: [(528, 10.0), (441, 10.0)]}\nsource = 29\ntarget = 796\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 755, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {898: [(253, 0.0)], 454: [(253, 0.0)], 511: [(253, 0.0)], 514: [(454, 15.0), (898, 5.0), (511, 30.0)], 485: [(454, 15.0), (898, 5.0), (511, 30.0)], 253: [], 744: [(514, 60.0), (485, 45.0)]}\nsource = 744\ntarget = 253\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 756, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {545: [(129, 20.0), (252, 25.0), (400, 30.0), (249, 15.0)], 400: [(319, 0.0)], 249: [(319, 0.0)], 129: [(319, 0.0)], 252: [(319, 0.0)], 319: [], 697: [(545, 10.0)]}\nsource = 697\ntarget = 319\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 757, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {595: [(97, 2.0), (774, 3.0), (968, 1.0)], 948: [(937, 0.0)], 968: [(937, 0.0)], 97: [(937, 0.0)], 774: [(937, 0.0)], 937: [], 776: [(595, 1.0), (948, 0.5)]}\nsource = 776\ntarget = 937\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 758, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {672: [(7, 2.0)], 324: [(613, 6.0)], 7: [(613, 6.0)], 613: [(820, 3.0)], 820: [(67, 0.0)], 67: [], 720: [(672, 10.0), (324, 2.0)]}\nsource = 720\ntarget = 67\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 759, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {842: [(171, 60.0)], 171: [(403, 0.0)], 506: [(403, 0.0)], 628: [(403, 0.0)], 361: [(403, 0.0)], 403: [], 764: [(842, 45.0), (506, 30.0), (628, 30.0), (361, 960.0)]}\nsource = 764\ntarget = 403\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 960.0, "source_answer": 960.0}
{"source_row": 760, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {586: [(379, 0.0)], 730: [(586, 5.0)], 98: [(586, 5.0)], 978: [(410, 10.0)], 410: [(586, 5.0)], 379: [], 797: [(730, 30.0), (98, 15.0), (978, 20.0)]}\nsource = 797\ntarget = 379\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 761, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {808: [(938, 15.0)], 328: [(938, 15.0)], 938: [(401, 0.0)], 401: [], 157: [(808, 5.0), (328, 10.0)]}\nsource = 157\ntarget = 401\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 762, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {147: [(603, 1.0)], 706: [(603, 1.0)], 563: [(603, 1.0)], 603: [(602, 1.0)], 602: [(912, 0.0)], 912: [], 154: [(147, 5.0), (706, 3.0), (563, 2.0)]}\nsource = 154\ntarget = 912\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 763, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {808: [(389, 120.0)], 835: [(389, 120.0)], 864: [(389, 120.0)], 389: [(748, 0.0)], 748: [], 576: [(808, 2.0), (835, 1.0), (864, 1.0)]}\nsource = 576\ntarget = 748\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 122.0, "source_answer": 122.0}
{"source_row": 764, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {726: [(61, 3.0)], 258: [(61, 3.0)], 61: [(718, 5.0)], 718: [(874, 10.0)], 874: [(238, 0.0)], 238: [], 368: [(726, 5.0), (258, 2.0)]}\nsource = 368\ntarget = 238\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.0, "source_answer": 23.0}
{"source_row": 765, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {503: [(849, 0.0)], 923: [(981, 259200.0)], 981: [(849, 0.0)], 849: [], 259: [(503, 20160.0), (923, 172800.0)]}\nsource = 259\ntarget = 849\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 432000.0, "source_answer": 432000.0}
{"source_row": 766, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {968: [(223, 15.0)], 227: [(223, 15.0)], 388: [(223, 15.0)], 223: [(578, 0.0)], 578: [], 102: [(968, 5.0), (227, 2.0), (388, 10.0)]}\nsource = 102\ntarget = 578\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 767, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {798: [(439, 5.0)], 131: [(439, 5.0)], 439: [(487, 0.0)], 487: [], 847: [(798, 10.0), (131, 2.0)]}\nsource = 847\ntarget = 487\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 768, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(272, 0.0)], 675: [(272, 0.0)], 370: [(903, 20.0)], 903: [(272, 0.0)], 224: [(272, 0.0)], 272: [], 23: [(881, 30.0), (675, 10.0), (370, 15.0), (224, 5.0)]}\nsource = 23\ntarget = 272\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 769, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {716: [(121, 0.0)], 921: [(632, 1.0)], 632: [(121, 0.0)], 121: [], 873: [(716, 1.0), (921, 2.0)]}\nsource = 873\ntarget = 121\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 770, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {548: [(79, 3.0), (50, 3.0)], 79: [(49, 0.0)], 50: [(49, 0.0)], 49: [], 437: [(548, 2.0)]}\nsource = 437\ntarget = 49\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 771, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {462: [(298, 0.0)], 738: [(857, 3.0)], 857: [(298, 0.0)], 298: [], 369: [(462, 5.0), (738, 2.0)]}\nsource = 369\ntarget = 298\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 772, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {494: [(872, 1440.0)], 872: [(330, 20160.0)], 851: [(407, 0.0)], 330: [(407, 0.0)], 407: [], 572: [(494, 20160.0), (851, 30240.0)]}\nsource = 572\ntarget = 407\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41760.0, "source_answer": 41760.0}
{"source_row": 773, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {521: [(160, 0.0)], 396: [(160, 0.0)], 937: [(160, 0.0)], 640: [(937, 129600.0)], 160: [], 510: [(521, 259200.0), (396, 518400.0), (640, 518400.0)]}\nsource = 510\ntarget = 160\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 648000.0, "source_answer": 648000.0}
{"source_row": 774, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {742: [(428, 0.0)], 593: [(943, 60.0)], 943: [(428, 0.0)], 162: [(428, 0.0)], 979: [(428, 0.0)], 428: [], 48: [(742, 60.0), (593, 30.0), (162, 30.0), (979, 60.0)]}\nsource = 48\ntarget = 428\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 775, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {672: [(883, 15.0)], 832: [(883, 15.0)], 883: [(611, 4320.0)], 611: [(708, 30240.0)], 708: [(982, 0.0)], 982: [], 218: [(672, 30.0), (832, 60.0)]}\nsource = 218\ntarget = 982\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34635.0, "source_answer": 34635.0}
{"source_row": 776, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {307: [(711, 2.0)], 711: [(14, 0.0)], 495: [(14, 0.0)], 14: [], 525: [(307, 10.0), (495, 5.0)]}\nsource = 525\ntarget = 14\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 777, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {731: [(364, 0.17), (726, 0.08)], 364: [(375, 0.0)], 726: [(375, 0.0)], 375: [], 508: [(731, 0.03)]}\nsource = 508\ntarget = 375\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.2, "source_answer": 0.2}
{"source_row": 778, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {508: [(559, 2.0)], 30: [(559, 2.0)], 559: [(405, 30.0)], 405: [(719, 1.0)], 719: [(762, 0.0)], 762: [], 86: [(508, 15.0), (30, 5.0)]}\nsource = 86\ntarget = 762\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 48.0, "source_answer": 48.0}
{"source_row": 779, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {802: [(166, 0.0)], 226: [(166, 0.0)], 124: [(860, 2.0)], 462: [(166, 0.0)], 860: [(166, 0.0)], 166: [], 498: [(802, 1.0), (226, 1.0), (124, 1.0), (462, 2.0)]}\nsource = 498\ntarget = 166\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 780, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {904: [(259, 5.0)], 234: [(318, 0.0)], 187: [(318, 0.0)], 424: [(318, 0.0)], 259: [(318, 0.0)], 318: [], 676: [(904, 10.0), (234, 10.0), (187, 15.0), (424, 10.0)]}\nsource = 676\ntarget = 318\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 781, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {759: [(249, 0.0)], 734: [(38, 40320.0)], 38: [(249, 0.0)], 249: [], 582: [(759, 30240.0), (734, 86400.0)]}\nsource = 582\ntarget = 249\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 126720.0, "source_answer": 126720.0}
{"source_row": 782, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {534: [(380, 3.0), (930, 10.0)], 380: [(480, 2.0)], 930: [(480, 2.0)], 480: [(740, 5.0)], 740: [(254, 0.0)], 254: [], 363: [(534, 5.0)]}\nsource = 363\ntarget = 254\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 783, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {71: [(597, 1.0)], 97: [(896, 0.0)], 251: [(896, 0.0)], 597: [(896, 0.0)], 771: [(896, 0.0)], 896: [], 756: [(71, 5.0), (97, 2.0), (251, 1.0), (771, 3.0)]}\nsource = 756\ntarget = 896\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 784, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {169: [(87, 180.0)], 878: [(592, 0.0)], 87: [(592, 0.0)], 592: [], 818: [(169, 60.0), (878, 60.0)]}\nsource = 818\ntarget = 592\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 785, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {532: [(375, 15.0)], 816: [(135, 0.0)], 375: [(135, 0.0)], 240: [(135, 0.0)], 457: [(135, 0.0)], 135: [], 418: [(532, 5.0), (816, 15.0), (240, 30.0), (457, 20.0)]}\nsource = 418\ntarget = 135\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 786, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {237: [(384, 5.0), (532, 10.0)], 791: [(109, 0.0)], 264: [(791, 2.0)], 384: [(264, 2.0)], 532: [(264, 2.0)], 109: [], 177: [(237, 5.0)]}\nsource = 177\ntarget = 109\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19.0, "source_answer": 19.0}
{"source_row": 787, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {234: [(292, 20.0)], 959: [(415, 1.0)], 415: [(357, 1.0)], 357: [(292, 20.0)], 292: [(498, 0.0)], 498: [], 23: [(234, 5.0), (959, 2.0)]}\nsource = 23\ntarget = 498\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 788, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {783: [(790, 0.0)], 910: [(952, 5.0), (120, 5.0)], 916: [(952, 5.0), (120, 5.0)], 952: [(790, 0.0)], 120: [(790, 0.0)], 790: [], 884: [(783, 10.0), (910, 15.0), (916, 5.0)]}\nsource = 884\ntarget = 790\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 789, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {680: [(723, 1.0)], 120: [(723, 1.0)], 723: [(756, 2.0)], 756: [(340, 1.0)], 340: [(204, 0.0)], 204: [], 454: [(680, 5.0), (120, 1.0)]}\nsource = 454\ntarget = 204\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 790, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {951: [(707, 1.0), (635, 2.0)], 707: [(879, 10.0)], 635: [(879, 10.0)], 879: [(578, 0.0)], 578: [(787, 0.0)], 787: [], 260: [(951, 5.0)]}\nsource = 260\ntarget = 787\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 791, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {95: [(756, 1.0)], 260: [(756, 1.0)], 756: [(931, 5.0)], 931: [(245, 5.0)], 245: [(918, 0.0)], 918: [], 267: [(95, 5.0), (260, 2.0)]}\nsource = 267\ntarget = 918\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 792, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {974: [(759, 0.0)], 24: [(780, 60.0)], 942: [(759, 0.0)], 884: [(759, 0.0)], 780: [(759, 0.0)], 759: [], 826: [(974, 30.0), (24, 60.0), (942, 120.0), (884, 60.0)]}\nsource = 826\ntarget = 759\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 793, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {722: [(219, 15.0), (542, 10.0), (710, 5.0), (635, 5.0)], 710: [(175, 0.0)], 635: [(175, 0.0)], 219: [(175, 0.0)], 542: [(175, 0.0)], 175: [], 428: [(722, 10.0)]}\nsource = 428\ntarget = 175\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 794, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {207: [(505, 1.0)], 505: [(526, 5.0)], 575: [(526, 5.0)], 580: [(526, 5.0)], 526: [(884, 0.0)], 884: [], 227: [(207, 2.0), (575, 3.0), (580, 1.0)]}\nsource = 227\ntarget = 884\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 795, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {9: [(540, 0.0)], 737: [(9, 30.0)], 47: [(540, 0.0)], 15: [(540, 0.0)], 540: [], 658: [(737, 15.0), (47, 20.0), (15, 15.0)]}\nsource = 658\ntarget = 540\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 796, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {575: [(844, 3.0)], 529: [(844, 3.0)], 844: [(862, 2.0)], 862: [(518, 0.0)], 518: [], 446: [(575, 5.0), (529, 2.0)]}\nsource = 446\ntarget = 518\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 797, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {91: [(812, 20.0)], 163: [(812, 20.0)], 812: [(907, 15.0)], 907: [(174, 5.0)], 174: [(51, 0.0)], 51: [], 60: [(91, 30.0), (163, 10.0)]}\nsource = 60\ntarget = 51\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 798, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {406: [(404, 60480.0), (494, 86400.0), (485, 86400.0), (738, 129600.0)], 485: [(383, 0.0)], 738: [(383, 0.0)], 404: [(383, 0.0)], 494: [(383, 0.0)], 383: [], 974: [(406, 20160.0)]}\nsource = 974\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 149760.0, "source_answer": 149760.0}
{"source_row": 799, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {612: [(911, 60.0)], 685: [(746, 15.0)], 746: [(911, 60.0)], 911: [(779, 10.0)], 779: [(333, 0.0)], 333: [], 437: [(612, 120.0), (685, 30.0)]}\nsource = 437\ntarget = 333\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 190.0, "source_answer": 190.0}
{"source_row": 800, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {661: [(370, 0.0)], 160: [(661, 30.0)], 2: [(661, 30.0)], 115: [(661, 30.0)], 683: [(661, 30.0)], 370: [], 520: [(160, 120.0), (2, 30.0), (115, 120.0), (683, 120.0)]}\nsource = 520\ntarget = 370\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 801, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {841: [(8, 2.0)], 8: [(81, 0.0)], 782: [(81, 0.0)], 379: [(81, 0.0)], 81: [], 684: [(841, 5.0), (782, 10.0), (379, 5.0)]}\nsource = 684\ntarget = 81\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 802, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {348: [(934, 8.0)], 127: [(108, 15.0)], 934: [(584, 0.0)], 108: [(584, 0.0)], 584: [], 501: [(348, 5.0), (127, 10.0)]}\nsource = 501\ntarget = 584\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 803, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {189: [(213, 3.0)], 213: [(477, 0.0)], 477: [(498, 0.0)], 819: [(498, 0.0)], 498: [(522, 0.0)], 522: [], 594: [(189, 5.0), (819, 3.0)]}\nsource = 594\ntarget = 522\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 804, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {99: [(92, 15.0), (100, 5.0)], 92: [(938, 10.0)], 100: [(938, 10.0)], 938: [(825, 10.0)], 825: [(906, 0.0)], 906: [], 871: [(99, 10.0)]}\nsource = 871\ntarget = 906\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 805, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {624: [(360, 2.0)], 414: [(360, 2.0)], 360: [(175, 20.0)], 175: [(96, 0.0)], 96: [], 976: [(624, 5.0), (414, 2.0)]}\nsource = 976\ntarget = 96\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 806, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {66: [(342, 15.0)], 116: [(342, 15.0)], 893: [(342, 15.0)], 342: [(410, 30.0)], 410: [(999, 0.0)], 999: [], 333: [(66, 10.0), (116, 5.0), (893, 10.0)]}\nsource = 333\ntarget = 999\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 807, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {663: [(552, 0.0)], 42: [(657, 5.0)], 657: [(530, 15.0)], 530: [(552, 0.0)], 552: [], 323: [(663, 5.0), (42, 10.0)]}\nsource = 323\ntarget = 552\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 808, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {678: [(221, 240.0)], 216: [(221, 240.0)], 754: [(129, 0.0)], 221: [(562, 30.0), (754, 15.0)], 562: [(129, 0.0)], 129: [], 57: [(678, 300.0), (216, 60.0)]}\nsource = 57\ntarget = 129\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 570.0, "source_answer": 570.0}
{"source_row": 809, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {122: [(324, 60.0), (416, 5.0)], 694: [(122, 30.0)], 750: [(556, 0.0)], 324: [(750, 45.0)], 416: [(750, 45.0)], 556: [], 395: [(694, 60.0)]}\nsource = 395\ntarget = 556\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 195.0, "source_answer": 195.0}
{"source_row": 810, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {656: [(382, 3.0)], 401: [(382, 3.0)], 87: [(159, 0.0)], 382: [(159, 0.0)], 728: [(159, 0.0)], 159: [], 677: [(656, 5.0), (401, 2.0), (87, 3.0), (728, 3.0)]}\nsource = 677\ntarget = 159\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 811, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {401: [(856, 15.0)], 508: [(856, 15.0)], 856: [(390, 0.0)], 312: [(117, 20.0)], 117: [(401, 30.0)], 390: [], 594: [(508, 5.0), (312, 20.0)]}\nsource = 594\ntarget = 390\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 812, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {111: [(416, 0.0)], 908: [(416, 0.0)], 236: [(416, 0.0)], 653: [(908, 10.0), (111, 5.0), (236, 5.0)], 416: [], 509: [(653, 5.0)]}\nsource = 509\ntarget = 416\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 813, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {183: [(265, 2.0)], 265: [(358, 3.0)], 358: [(739, 1.0)], 38: [(750, 0.0)], 739: [(750, 0.0)], 750: [], 545: [(183, 5.0), (38, 5.0)]}\nsource = 545\ntarget = 750\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 814, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {727: [(834, 7.0)], 834: [(81, 240.0)], 81: [(446, 5.0)], 994: [(707, 0.0)], 446: [(707, 0.0)], 707: [], 539: [(727, 10.0), (994, 15.0)]}\nsource = 539\ntarget = 707\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 262.0, "source_answer": 262.0}
{"source_row": 815, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {466: [(226, 300.0)], 282: [(176, 0.0)], 226: [(176, 0.0)], 431: [(176, 0.0)], 176: [], 581: [(466, 120.0), (282, 60.0), (431, 60.0)]}\nsource = 581\ntarget = 176\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 420.0, "source_answer": 420.0}
{"source_row": 816, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {499: [(448, 2.0)], 243: [(448, 2.0)], 448: [(137, 1440.0)], 137: [(862, 0.0)], 862: [], 21: [(499, 5.0), (243, 2.0)]}\nsource = 21\ntarget = 862\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1447.0, "source_answer": 1447.0}
{"source_row": 817, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {226: [(440, 3.0)], 440: [(990, 3.0)], 220: [(990, 3.0)], 990: [(753, 1.0)], 753: [(784, 0.0)], 784: [], 522: [(226, 5.0), (220, 2.0)]}\nsource = 522\ntarget = 784\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 818, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {990: [(35, 5.0)], 35: [(907, 0.0)], 71: [(907, 0.0)], 372: [(907, 0.0)], 291: [(907, 0.0)], 907: [], 704: [(990, 15.0), (71, 2.0), (372, 15.0), (291, 480.0)]}\nsource = 704\ntarget = 907\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 480.0, "source_answer": 480.0}
{"source_row": 819, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {823: [(529, 3.0)], 373: [(296, 12.0)], 296: [(307, 2.0)], 307: [(529, 3.0)], 529: [(847, 0.0)], 847: [], 790: [(823, 5.0), (373, 5.0)]}\nsource = 790\ntarget = 847\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 820, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {459: [(181, 3.0), (941, 8.0)], 181: [(0, 10.0)], 941: [(0, 10.0)], 0: [(261, 15.0)], 261: [(721, 0.0)], 721: [], 925: [(459, 5.0)]}\nsource = 925\ntarget = 721\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 38.0, "source_answer": 38.0}
{"source_row": 821, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {360: [(283, 5.0)], 919: [(39, 20.0)], 283: [(39, 20.0)], 39: [(282, 20.0)], 282: [(198, 0.0)], 198: [], 863: [(360, 30.0), (919, 15.0)]}\nsource = 863\ntarget = 198\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 822, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {96: [(300, 0.0)], 226: [(96, 10.0)], 874: [(96, 10.0)], 300: [], 813: [(226, 120.0), (874, 30.0)]}\nsource = 813\ntarget = 300\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130.0, "source_answer": 130.0}
{"source_row": 823, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {152: [(836, 1.0)], 892: [(836, 1.0)], 836: [(256, 2.0)], 256: [(754, 5.0)], 754: [(292, 0.0)], 292: [], 759: [(152, 1.0), (892, 1.0)]}\nsource = 759\ntarget = 292\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 824, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {942: [(977, 45.0)], 977: [(706, 0.0)], 390: [(977, 45.0)], 706: [], 843: [(942, 60.0), (390, 120.0)]}\nsource = 843\ntarget = 706\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 165.0, "source_answer": 165.0}
{"source_row": 825, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(230, 12.0)], 360: [(727, 1.0)], 727: [(230, 12.0)], 230: [(46, 5.0)], 46: [(800, 0.0)], 800: [], 345: [(963, 5.0), (360, 2.0)]}\nsource = 345\ntarget = 800\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 826, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {214: [(320, 2.0)], 320: [(75, 1.0)], 75: [(348, 0.0)], 781: [(352, 10.0)], 352: [(348, 0.0)], 348: [], 56: [(214, 1.0), (781, 7200.0)]}\nsource = 56\ntarget = 348\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7210.0, "source_answer": 7210.0}
{"source_row": 827, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {898: [(920, 10.0)], 920: [(36, 10.0), (140, 5.0)], 140: [(278, 15.0)], 36: [(278, 15.0)], 278: [(952, 0.0)], 952: [], 362: [(898, 5.0)]}\nsource = 362\ntarget = 952\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 828, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {129: [(424, 3.0)], 119: [(424, 3.0)], 424: [(386, 1.0)], 386: [(339, 1440.0)], 339: [(193, 0.0)], 193: [], 308: [(129, 5.0), (119, 2.0)]}\nsource = 308\ntarget = 193\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1449.0, "source_answer": 1449.0}
{"source_row": 829, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {756: [(735, 10.0)], 735: [(241, 1.0)], 216: [(241, 1.0)], 241: [(742, 1.0)], 742: [(616, 0.0)], 616: [], 6: [(756, 5.0), (216, 2.0)]}\nsource = 6\ntarget = 616\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 830, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {666: [(27, 3.0)], 128: [(669, 15.0)], 669: [(27, 3.0)], 27: [(945, 2.0)], 945: [(40, 0.0)], 40: [], 351: [(666, 10.0), (128, 2.0)]}\nsource = 351\ntarget = 40\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 831, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(133, 0.0)], 27: [(334, 2.0)], 334: [(133, 0.0)], 133: [], 987: [(316, 5.0), (27, 10.0)]}\nsource = 987\ntarget = 133\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 832, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {374: [(788, 30.0)], 462: [(788, 30.0)], 127: [(150, 15.0)], 788: [(150, 15.0)], 150: [(832, 0.0)], 832: [], 226: [(374, 30.0), (462, 60.0), (127, 15.0)]}\nsource = 226\ntarget = 832\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 833, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(122, 0.0)], 950: [(122, 0.0)], 79: [(122, 0.0)], 927: [(122, 0.0)], 667: [(927, 10.0), (589, 15.0), (950, 10.0), (79, 10.0)], 122: [], 4: [(667, 5.0)]}\nsource = 4\ntarget = 122\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 834, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {500: [(200, 0.0)], 220: [(200, 0.0)], 343: [(200, 0.0)], 323: [(569, 1.0)], 569: [(200, 0.0)], 200: [], 678: [(500, 1.0), (220, 1.0), (343, 1.0), (323, 2.0)]}\nsource = 678\ntarget = 200\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 835, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {419: [(458, 5.0)], 827: [(672, 2.0)], 672: [(458, 5.0)], 458: [(989, 120.0)], 989: [(438, 0.0)], 438: [], 622: [(419, 5.0), (827, 10.0)]}\nsource = 622\ntarget = 438\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 137.0, "source_answer": 137.0}
{"source_row": 836, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {964: [(827, 1.0)], 827: [(929, 1.0)], 929: [(249, 0.0)], 733: [(870, 2.0)], 870: [(249, 0.0)], 249: [], 510: [(964, 2.0), (733, 1.0)]}\nsource = 510\ntarget = 249\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 837, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {974: [(669, 0.5)], 669: [(562, 0.0)], 945: [(562, 0.0)], 815: [(562, 0.0)], 984: [(562, 0.0)], 562: [], 575: [(974, 10.0), (945, 30.0), (815, 10.0), (984, 5.0)]}\nsource = 575\ntarget = 562\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 838, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {722: [(108, 0.0)], 935: [(108, 0.0)], 641: [(935, 43200.0)], 115: [(108, 0.0)], 255: [(108, 0.0)], 108: [], 18: [(722, 10.0), (641, 10080.0), (115, 20160.0), (255, 20.0)]}\nsource = 18\ntarget = 108\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 53280.0, "source_answer": 53280.0}
{"source_row": 839, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {397: [(830, 2.0), (174, 2.0), (216, 3.0), (921, 2.0)], 216: [(220, 0.0)], 921: [(220, 0.0)], 830: [(220, 0.0)], 174: [(220, 0.0)], 220: [], 380: [(397, 2.0)]}\nsource = 380\ntarget = 220\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 840, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {642: [(216, 5.0)], 131: [(216, 5.0)], 229: [(216, 5.0)], 216: [(49, 0.0)], 49: [], 878: [(642, 5.0), (131, 10.0), (229, 10.0)]}\nsource = 878\ntarget = 49\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 841, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {66: [(888, 3.0), (947, 2.0)], 888: [(96, 0.0)], 947: [(96, 0.0)], 96: [], 296: [(66, 5.0)]}\nsource = 296\ntarget = 96\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 842, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {219: [(171, 120.0), (50, 120.0)], 171: [(220, 60.0)], 50: [(220, 60.0)], 220: [(451, 30.0)], 451: [(597, 0.0)], 597: [], 489: [(219, 60.0)]}\nsource = 489\ntarget = 597\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 270.0, "source_answer": 270.0}
{"source_row": 843, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {725: [(40, 0.2), (22, 0.1), (882, 0.17), (199, 0.13)], 882: [(566, 0.0)], 199: [(566, 0.0)], 40: [(566, 0.0)], 22: [(566, 0.0)], 566: [], 154: [(725, 0.08)]}\nsource = 154\ntarget = 566\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.28, "source_answer": 0.28}
{"source_row": 844, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {353: [(936, 15.0)], 936: [(460, 0.0)], 524: [(460, 0.0)], 317: [(135, 30.0)], 135: [(460, 0.0)], 460: [], 581: [(353, 30.0), (524, 15.0), (317, 20.0)]}\nsource = 581\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 845, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {40: [(90, 1.0)], 90: [(750, 2.0)], 750: [(958, 3.0), (368, 2.0)], 958: [(783, 0.0)], 368: [(783, 0.0)], 783: [], 51: [(40, 2.0)]}\nsource = 51\ntarget = 783\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 846, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {8: [(83, 30.0)], 444: [(83, 30.0)], 83: [(844, 2.0)], 844: [(267, 0.0)], 267: [], 734: [(8, 5.0), (444, 1.0)]}\nsource = 734\ntarget = 267\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 847, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {804: [(30, 1.0)], 439: [(30, 1.0)], 30: [(362, 0.0)], 362: [], 257: [(804, 720.0), (439, 2.0)]}\nsource = 257\ntarget = 362\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 721.0, "source_answer": 721.0}
{"source_row": 848, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {740: [(589, 0.08)], 685: [(691, 0.0)], 589: [(691, 0.0)], 691: [], 656: [(740, 0.03), (685, 0.05)]}\nsource = 656\ntarget = 691\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.11, "source_answer": 0.11}
{"source_row": 849, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {573: [(610, 0.0)], 526: [(782, 10.0)], 497: [(689, 5.0)], 782: [(497, 15.0)], 689: [(610, 0.0)], 610: [], 243: [(573, 30.0), (526, 5.0)]}\nsource = 243\ntarget = 610\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 850, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {781: [(948, 0.0)], 728: [(948, 0.0)], 298: [(948, 0.0)], 323: [(514, 15.0)], 514: [(948, 0.0)], 948: [], 597: [(781, 15.0), (728, 10.0), (298, 15.0), (323, 20.0)]}\nsource = 597\ntarget = 948\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 851, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {263: [(810, 3.0)], 810: [(728, 30.0)], 728: [(596, 0.0)], 67: [(172, 2.0)], 172: [(810, 3.0)], 596: [], 241: [(263, 5.0), (67, 5.0)]}\nsource = 241\ntarget = 596\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 852, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {794: [(268, 7200.0)], 864: [(268, 7200.0)], 268: [(644, 0.0)], 514: [(268, 7200.0)], 237: [(268, 7200.0)], 644: [], 251: [(794, 10080.0), (864, 20160.0), (514, 7200.0), (237, 10080.0)]}\nsource = 251\ntarget = 644\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27360.0, "source_answer": 27360.0}
{"source_row": 853, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {444: [(635, 30.0), (178, 1440.0)], 866: [(635, 30.0), (178, 1440.0)], 13: [(635, 30.0), (178, 1440.0)], 635: [(124, 0.0)], 178: [(124, 0.0)], 124: [], 947: [(444, 5.0), (866, 10.0), (13, 5.0)]}\nsource = 947\ntarget = 124\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1450.0, "source_answer": 1450.0}
{"source_row": 854, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {77: [(575, 0.0)], 720: [(77, 10.0)], 983: [(575, 0.0)], 409: [(575, 0.0)], 575: [], 612: [(720, 5.0), (983, 15.0), (409, 5.0)]}\nsource = 612\ntarget = 575\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 855, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {876: [(972, 0.17)], 972: [(548, 0.33)], 548: [(581, 0.17), (49, 0.17)], 581: [(966, 0.0)], 49: [(966, 0.0)], 966: [], 835: [(876, 1.0)]}\nsource = 835\ntarget = 966\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.67, "source_answer": 1.67}
{"source_row": 856, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {129: [(529, 0.0)], 728: [(371, 30.0), (129, 30.0)], 371: [(529, 0.0)], 529: [], 848: [(728, 20160.0)]}\nsource = 848\ntarget = 529\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20190.0, "source_answer": 20190.0}
{"source_row": 857, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {985: [(782, 0.0)], 193: [(70, 2.0)], 562: [(70, 2.0)], 70: [(647, 1.0)], 647: [(782, 0.0)], 782: [], 463: [(985, 2.0), (193, 1.0), (562, 1.0)]}\nsource = 463\ntarget = 782\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 858, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {445: [(119, 1.0)], 770: [(119, 1.0)], 119: [(724, 0.0)], 724: [], 928: [(445, 2.0), (770, 1.0)]}\nsource = 928\ntarget = 724\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 859, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {143: [(901, 60480.0), (973, 40320.0), (282, 20160.0), (584, 40320.0)], 282: [(987, 0.0)], 584: [(987, 0.0)], 901: [(987, 0.0)], 973: [(987, 0.0)], 987: [], 317: [(143, 5.0)]}\nsource = 317\ntarget = 987\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60485.0, "source_answer": 60485.0}
{"source_row": 860, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {332: [(706, 30.0), (615, 30.0), (552, 120.0), (127, 180.0)], 552: [(890, 0.0)], 127: [(890, 0.0)], 706: [(890, 0.0)], 615: [(890, 0.0)], 890: [], 224: [(332, 30.0)]}\nsource = 224\ntarget = 890\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 210.0, "source_answer": 210.0}
{"source_row": 861, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {183: [(682, 10.0)], 693: [(183, 10.0)], 682: [(817, 0.0)], 632: [(817, 0.0)], 817: [], 452: [(693, 20.0), (632, 2.0)]}\nsource = 452\ntarget = 817\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 862, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {169: [(254, 0.0)], 284: [(254, 0.0)], 79: [(254, 0.0)], 823: [(79, 60.0)], 254: [], 730: [(169, 30.0), (284, 15.0), (823, 45.0)]}\nsource = 730\ntarget = 254\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 863, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {385: [(878, 3.0)], 878: [(114, 0.0)], 877: [(114, 0.0)], 400: [(114, 0.0)], 114: [], 115: [(385, 5.0), (877, 525600.0), (400, 259200.0)]}\nsource = 115\ntarget = 114\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525600.0, "source_answer": 525600.0}
{"source_row": 864, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {493: [(395, 15.0)], 395: [(744, 5.0)], 290: [(212, 20.0)], 212: [(744, 5.0)], 744: [(660, 0.0)], 660: [], 576: [(493, 180.0), (290, 15.0)]}\nsource = 576\ntarget = 660\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 200.0, "source_answer": 200.0}
{"source_row": 865, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {521: [(869, 1.0)], 714: [(869, 1.0)], 869: [(18, 0.0)], 914: [(989, 1.0)], 989: [(714, 1.0), (521, 1.0)], 18: [], 522: [(914, 1.0)]}\nsource = 522\ntarget = 18\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 866, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {156: [(322, 120.0), (183, 120.0), (805, 300.0)], 183: [(257, 0.0)], 805: [(257, 0.0)], 145: [(257, 0.0)], 322: [(257, 0.0)], 257: [], 909: [(156, 60.0), (145, 300.0)]}\nsource = 909\ntarget = 257\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 360.0, "source_answer": 360.0}
{"source_row": 867, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {593: [(760, 10.0)], 760: [(633, 15.0)], 633: [(474, 0.0)], 988: [(511, 5.0)], 511: [(474, 0.0)], 474: [], 501: [(593, 10.0), (988, 5.0)]}\nsource = 501\ntarget = 474\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 868, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {123: [(14, 20.0), (614, 20.0)], 14: [(604, 0.0)], 614: [(604, 0.0)], 604: [], 305: [(123, 10.0)]}\nsource = 305\ntarget = 604\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 869, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {884: [(54, 10.0), (583, 10.0), (787, 10.0), (74, 15.0)], 787: [(802, 0.0)], 74: [(802, 0.0)], 54: [(802, 0.0)], 583: [(802, 0.0)], 802: [], 410: [(884, 30.0)]}\nsource = 410\ntarget = 802\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 870, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {368: [(999, 0.0)], 891: [(731, 5.0)], 731: [(18, 15.0)], 18: [(846, 2.0)], 846: [(999, 0.0)], 999: [], 964: [(368, 10.0), (891, 10.0)]}\nsource = 964\ntarget = 999\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 871, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {987: [(396, 0.0)], 13: [(396, 0.0)], 284: [(396, 0.0)], 731: [(987, 60.0)], 269: [(396, 0.0)], 396: [], 283: [(13, 4320.0), (284, 240.0), (731, 180.0), (269, 120.0)]}\nsource = 283\ntarget = 396\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4320.0, "source_answer": 4320.0}
{"source_row": 872, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {510: [(156, 1440.0)], 156: [(943, 5760.0), (192, 40320.0)], 192: [(917, 5.0)], 943: [(910, 0.0)], 917: [(910, 0.0)], 910: [], 980: [(510, 5.0)]}\nsource = 980\ntarget = 910\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41770.0, "source_answer": 41770.0}
{"source_row": 873, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {59: [(700, 0.0)], 975: [(59, 5.0)], 259: [(700, 0.0)], 819: [(700, 0.0)], 700: [], 997: [(975, 10.0), (259, 2.0), (819, 1.0)]}\nsource = 997\ntarget = 700\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 874, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {922: [(237, 2.0)], 237: [(813, 10.0)], 813: [(191, 0.0)], 428: [(191, 0.0)], 375: [(191, 0.0)], 191: [], 26: [(922, 5.0), (428, 5.0), (375, 5.0)]}\nsource = 26\ntarget = 191\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 875, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {639: [(377, 20.0)], 377: [(918, 30.0)], 184: [(918, 30.0)], 918: [(326, 10.0)], 326: [(240, 0.0)], 240: [], 605: [(639, 10.0), (184, 5.0)]}\nsource = 605\ntarget = 240\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 876, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {102: [(409, 10.0), (880, 2.0)], 409: [(629, 5.0)], 880: [(629, 5.0)], 629: [(730, 30.0)], 730: [(33, 0.0)], 33: [], 326: [(102, 5.0)]}\nsource = 326\ntarget = 33\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 877, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {596: [(940, 0.17)], 940: [(570, 0.08)], 570: [(586, 0.0)], 499: [(586, 0.0)], 872: [(586, 0.0)], 586: [], 191: [(596, 1.0), (499, 0.5), (872, 1.0)]}\nsource = 191\ntarget = 586\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.25, "source_answer": 1.25}
{"source_row": 878, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {133: [(275, 2880.0)], 275: [(157, 120.0)], 17: [(43, 0.0)], 157: [(249, 40320.0), (17, 2880.0)], 249: [(43, 0.0)], 43: [], 766: [(133, 20160.0)]}\nsource = 766\ntarget = 43\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63480.0, "source_answer": 63480.0}
{"source_row": 879, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {426: [(896, 10.0), (950, 20.0), (96, 15.0), (672, 5.0)], 96: [(60, 0.0)], 672: [(60, 0.0)], 896: [(60, 0.0)], 950: [(60, 0.0)], 60: [], 22: [(426, 15.0)]}\nsource = 22\ntarget = 60\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 880, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {981: [(689, 10.0)], 365: [(689, 10.0)], 732: [(689, 10.0)], 689: [(726, 15.0)], 726: [(759, 0.0)], 759: [], 758: [(981, 120.0), (365, 30.0), (732, 20.0)]}\nsource = 758\ntarget = 759\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 145.0, "source_answer": 145.0}
{"source_row": 881, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {741: [(271, 15.0)], 732: [(271, 15.0)], 271: [(669, 5.0)], 669: [(420, 5.0)], 420: [(799, 0.0)], 799: [], 962: [(741, 30.0), (732, 5.0)]}\nsource = 962\ntarget = 799\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 882, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {463: [(226, 3.0)], 860: [(874, 1.0)], 874: [(226, 3.0)], 226: [(209, 1.0)], 209: [(863, 1.0)], 863: [(965, 0.0)], 965: [], 278: [(463, 5.0), (860, 10.0)]}\nsource = 278\ntarget = 965\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 883, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {967: [(481, 2.0)], 987: [(491, 4.0)], 491: [(481, 2.0)], 481: [(429, 1.0)], 429: [(423, 0.0)], 423: [], 223: [(967, 5.0), (987, 10.0)]}\nsource = 223\ntarget = 423\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 884, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {732: [(341, 15.0), (902, 20.0)], 341: [(587, 0.0)], 902: [(587, 0.0)], 587: [], 338: [(732, 30.0)]}\nsource = 338\ntarget = 587\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 885, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {683: [(943, 40320.0), (373, 60480.0), (601, 14400.0), (14, 20160.0), (414, 10080.0)], 601: [(460, 0.0)], 14: [(460, 0.0)], 943: [(460, 0.0)], 414: [(460, 0.0)], 373: [(460, 0.0)], 460: [], 301: [(683, 1440.0)]}\nsource = 301\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 61920.0, "source_answer": 61920.0}
{"source_row": 886, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {395: [(25, 2.0)], 25: [(75, 2.0)], 75: [(453, 7.0)], 798: [(6, 120.0)], 453: [(980, 0.0)], 6: [(453, 7.0)], 980: [], 559: [(395, 12.0), (798, 7.0)]}\nsource = 559\ntarget = 980\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 134.0, "source_answer": 134.0}
{"source_row": 887, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {255: [(380, 1.0)], 745: [(380, 1.0)], 380: [(688, 3.0)], 688: [(10, 1.0)], 10: [(809, 1.0)], 809: [(960, 0.0)], 960: [], 950: [(255, 5.0), (745, 5.0)]}\nsource = 950\ntarget = 960\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 888, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {688: [(726, 5.0)], 726: [(867, 10.0)], 867: [(28, 5.0)], 28: [(62, 240.0)], 62: [(889, 0.0)], 733: [(889, 0.0)], 889: [], 502: [(688, 15.0), (733, 10.0)]}\nsource = 502\ntarget = 889\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 275.0, "source_answer": 275.0}
{"source_row": 889, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {943: [(285, 0.0)], 657: [(285, 0.0)], 707: [(912, 2.0)], 912: [(285, 0.0)], 668: [(285, 0.0)], 478: [(285, 0.0)], 285: [], 819: [(943, 5.0), (657, 2.0), (707, 3.0), (668, 10.0), (478, 2.0)]}\nsource = 819\ntarget = 285\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 890, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {980: [(520, 5.0)], 816: [(999, 10.0)], 999: [(19, 60.0)], 520: [(19, 60.0)], 19: [(246, 360.0)], 246: [(595, 0.0)], 595: [], 377: [(980, 30.0), (816, 15.0)]}\nsource = 377\ntarget = 595\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 455.0, "source_answer": 455.0}
{"source_row": 891, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {508: [(927, 0.0)], 761: [(69, 15.0)], 69: [(139, 20.0)], 139: [(753, 10.0)], 753: [(10, 5.0)], 10: [(927, 0.0)], 927: [], 621: [(508, 30.0), (761, 10.0)]}\nsource = 621\ntarget = 927\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 892, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {941: [(209, 30.0), (48, 30.0)], 209: [(414, 0.0)], 48: [(414, 0.0)], 190: [(414, 0.0)], 414: [], 633: [(941, 60.0), (190, 60.0)]}\nsource = 633\ntarget = 414\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 893, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {728: [(151, 0.08)], 151: [(483, 0.0)], 767: [(454, 0.08)], 596: [(454, 0.08)], 454: [(583, 0.05)], 583: [(483, 0.0)], 483: [], 736: [(728, 0.17), (767, 0.05), (596, 0.03)]}\nsource = 736\ntarget = 483\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.25, "source_answer": 0.25}
{"source_row": 894, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {626: [(667, 5.0)], 667: [(931, 3.0)], 697: [(931, 3.0)], 931: [(921, 30.0)], 921: [(996, 5.0)], 996: [(69, 0.0)], 69: [], 694: [(626, 5.0), (697, 2.0)]}\nsource = 694\ntarget = 69\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 48.0, "source_answer": 48.0}
{"source_row": 895, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {782: [(849, 15.0)], 359: [(525, 10.0)], 738: [(525, 10.0)], 525: [(849, 15.0)], 849: [(162, 20.0)], 162: [(827, 0.0)], 827: [], 694: [(782, 15.0), (359, 10.0), (738, 5.0)]}\nsource = 694\ntarget = 827\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 896, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {484: [(773, 5.0)], 798: [(773, 5.0)], 331: [(773, 5.0)], 773: [(175, 2.0)], 175: [(91, 0.0)], 91: [], 39: [(484, 30.0), (798, 15.0), (331, 25.0)]}\nsource = 39\ntarget = 91\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 897, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {304: [(95, 5.0)], 95: [(600, 0.0)], 394: [(600, 0.0)], 94: [(600, 0.0)], 132: [(600, 0.0)], 600: [], 445: [(304, 15.0), (394, 15.0), (94, 5.0), (132, 30.0)]}\nsource = 445\ntarget = 600\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 898, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {304: [(999, 10.0)], 513: [(999, 10.0)], 999: [(587, 5.0)], 450: [(587, 5.0)], 587: [(584, 1.0)], 584: [(569, 0.0)], 569: [], 915: [(304, 5.0), (513, 10.0), (450, 2.0)]}\nsource = 915\ntarget = 569\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 26.0, "source_answer": 26.0}
{"source_row": 899, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {137: [(25, 5.0)], 254: [(25, 5.0)], 25: [(914, 15.0)], 914: [(107, 0.0)], 107: [], 345: [(137, 10.0), (254, 5.0)]}\nsource = 345\ntarget = 107\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 900, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {606: [(397, 30.0), (28, 60.0), (32, 60.0), (172, 60.0), (106, 30.0)], 32: [(267, 0.0)], 172: [(267, 0.0)], 397: [(267, 0.0)], 106: [(267, 0.0)], 28: [(267, 0.0)], 267: [], 193: [(606, 60.0)]}\nsource = 193\ntarget = 267\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 901, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {916: [(346, 2.0)], 346: [(210, 5.0)], 210: [(435, 3.0)], 597: [(210, 5.0)], 435: [(141, 0.0)], 141: [], 760: [(916, 5.0), (597, 10.0)]}\nsource = 760\ntarget = 141\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 902, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {283: [(375, 1.0)], 534: [(375, 1.0)], 24: [(375, 1.0)], 375: [(613, 2880.0)], 613: [(812, 14400.0)], 812: [(487, 0.0)], 487: [], 120: [(283, 1.0), (534, 0.5), (24, 1.0)]}\nsource = 120\ntarget = 487\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17282.0, "source_answer": 17282.0}
{"source_row": 903, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {467: [(154, 5.0)], 996: [(154, 5.0)], 146: [(512, 0.0)], 962: [(512, 0.0)], 154: [(512, 0.0)], 512: [], 579: [(467, 5.0), (996, 10.0), (146, 15.0), (962, 20.0)]}\nsource = 579\ntarget = 512\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 904, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {731: [(438, 5.0)], 218: [(438, 5.0)], 438: [(362, 0.0)], 362: [], 136: [(731, 5.0), (218, 10.0)]}\nsource = 136\ntarget = 362\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 905, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {848: [(591, 1.0)], 591: [(311, 5.0)], 782: [(311, 5.0)], 311: [(449, 1.0)], 449: [(821, 2.0)], 821: [(86, 0.0)], 86: [], 49: [(848, 2.0), (782, 5.0)]}\nsource = 49\ntarget = 86\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 906, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {765: [(122, 86400.0)], 327: [(371, 0.0)], 122: [(371, 0.0)], 434: [(371, 0.0)], 371: [], 871: [(765, 129600.0), (327, 20160.0), (434, 259200.0)]}\nsource = 871\ntarget = 371\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 259200.0, "source_answer": 259200.0}
{"source_row": 907, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {776: [(365, 2.0)], 365: [(78, 5.0)], 78: [(220, 120.0)], 156: [(78, 5.0)], 220: [(690, 5.0)], 690: [(960, 0.0)], 960: [], 983: [(776, 10.0), (156, 15.0)]}\nsource = 983\ntarget = 960\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 145.0, "source_answer": 145.0}
{"source_row": 908, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {135: [(172, 0.0)], 704: [(172, 0.0)], 300: [(704, 30.0)], 172: [], 726: [(135, 5.0), (300, 60.0)]}\nsource = 726\ntarget = 172\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 909, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {214: [(23, 2.0)], 4: [(23, 2.0)], 23: [(383, 0.0)], 706: [(4, 10.0)], 383: [], 42: [(214, 5.0), (706, 15.0)]}\nsource = 42\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 910, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {146: [(933, 120.0)], 713: [(250, 2.0)], 250: [(933, 120.0)], 933: [(911, 30.0)], 911: [(595, 5.0)], 595: [(701, 0.0)], 701: [], 400: [(146, 10.0), (713, 10.0)]}\nsource = 400\ntarget = 701\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 167.0, "source_answer": 167.0}
{"source_row": 911, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {598: [(105, 3.0)], 105: [(303, 1.0)], 429: [(303, 1.0)], 303: [(366, 1.0)], 366: [(151, 1.0)], 151: [(545, 0.0)], 545: [], 852: [(598, 2.0), (429, 1.0)]}\nsource = 852\ntarget = 545\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 912, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {910: [(82, 10.0)], 82: [(635, 0.0)], 19: [(82, 10.0)], 684: [(82, 10.0)], 655: [(82, 10.0)], 125: [(82, 10.0)], 635: [], 145: [(910, 5.0), (19, 5.0), (684, 5.0), (655, 5.0), (125, 15.0)]}\nsource = 145\ntarget = 635\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 913, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {346: [(889, 2.0)], 889: [(381, 1.0)], 985: [(381, 1.0)], 381: [(822, 180.0)], 822: [(983, 1.0)], 983: [(623, 0.0)], 623: [], 617: [(346, 5.0), (985, 1.0)]}\nsource = 617\ntarget = 623\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 189.0, "source_answer": 189.0}
{"source_row": 914, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {245: [(120, 10.0)], 994: [(120, 10.0)], 726: [(120, 10.0)], 120: [(801, 0.0)], 801: [], 115: [(245, 5.0), (994, 5.0), (726, 5.0)]}\nsource = 115\ntarget = 801\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 915, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {712: [(651, 1.0)], 651: [(852, 5.0)], 852: [(396, 2.0)], 396: [(225, 1.0)], 180: [(225, 1.0)], 225: [(192, 0.0)], 192: [], 573: [(712, 5.0), (180, 1.0)]}\nsource = 573\ntarget = 192\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.0, "source_answer": 14.0}
{"source_row": 916, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {199: [(682, 1.0)], 564: [(682, 1.0)], 42: [(682, 1.0)], 575: [(682, 1.0)], 982: [(682, 1.0)], 682: [(843, 0.0)], 843: [], 890: [(199, 2.0), (564, 2.0), (42, 1.0), (575, 2.0), (982, 2.0)]}\nsource = 890\ntarget = 843\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 917, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {613: [(25, 30.0), (472, 3.0), (928, 5.0), (564, 10.0), (110, 20.0)], 928: [(407, 0.0)], 564: [(407, 0.0)], 25: [(407, 0.0)], 110: [(407, 0.0)], 472: [(407, 0.0)], 407: [], 79: [(613, 1.0)]}\nsource = 79\ntarget = 407\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0}
{"source_row": 918, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {247: [(502, 0.0)], 768: [(869, 10.0)], 869: [(502, 0.0)], 502: [], 525: [(247, 5.0), (768, 2.0)]}\nsource = 525\ntarget = 502\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 919, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {700: [(130, 15.0), (945, 20.0)], 130: [(76, 0.0)], 945: [(76, 0.0)], 76: [], 796: [(700, 5.0)]}\nsource = 796\ntarget = 76\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 920, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {197: [(958, 3.0)], 958: [(333, 5.0), (134, 30.0)], 134: [(152, 10.0)], 333: [(943, 3.0)], 152: [(943, 3.0)], 943: [(458, 0.0)], 458: [], 252: [(197, 2.0)]}\nsource = 252\ntarget = 458\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 48.0, "source_answer": 48.0}
{"source_row": 921, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {865: [(739, 3.0)], 739: [(911, 60480.0), (733, 1.0)], 733: [(572, 0.0)], 911: [(572, 0.0)], 572: [], 769: [(865, 5.0)]}\nsource = 769\ntarget = 572\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60488.0, "source_answer": 60488.0}
{"source_row": 922, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {844: [(611, 0.0)], 450: [(106, 4.0)], 907: [(106, 4.0)], 106: [(759, 1.0)], 759: [(611, 0.0)], 611: [], 932: [(844, 5.0), (450, 3.0), (907, 2.0)]}\nsource = 932\ntarget = 611\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 923, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {301: [(166, 60480.0)], 166: [(176, 518400.0)], 176: [(880, 0.0)], 455: [(880, 0.0)], 880: [], 200: [(301, 30240.0), (455, 259200.0)]}\nsource = 200\ntarget = 880\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 609120.0, "source_answer": 609120.0}
{"source_row": 924, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {860: [(100, 120.0)], 940: [(860, 30.0)], 100: [(715, 0.0)], 804: [(860, 30.0)], 715: [], 433: [(940, 60.0), (804, 120.0)]}\nsource = 433\ntarget = 715\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 270.0, "source_answer": 270.0}
{"source_row": 925, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {622: [(576, 10.0)], 576: [(380, 20.0)], 434: [(380, 20.0)], 55: [(322, 0.0)], 380: [(322, 0.0)], 322: [], 206: [(622, 5.0), (434, 2.0), (55, 5.0)]}\nsource = 206\ntarget = 322\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 926, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {449: [(126, 10.0)], 126: [(38, 60.0)], 38: [(240, 0.0)], 918: [(126, 10.0)], 240: [], 707: [(449, 5.0), (918, 5.0)]}\nsource = 707\ntarget = 240\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 927, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {485: [(420, 3.0)], 420: [(324, 5.0)], 41: [(324, 5.0)], 324: [(859, 2.0)], 859: [(429, 1.0)], 429: [(839, 0.0)], 839: [], 240: [(485, 5.0), (41, 2.0)]}\nsource = 240\ntarget = 839\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 928, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {970: [(568, 20.0)], 642: [(230, 15.0)], 230: [(48, 5.0)], 48: [(568, 20.0)], 568: [(425, 0.0)], 425: [], 139: [(970, 5.0), (642, 10.0)]}\nsource = 139\ntarget = 425\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 929, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {797: [(584, 1.0), (318, 1.0), (107, 0.5), (873, 1.0)], 107: [(315, 0.0)], 873: [(315, 0.0)], 584: [(315, 0.0)], 318: [(315, 0.0)], 315: [], 666: [(797, 1.0)]}\nsource = 666\ntarget = 315\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.0, "source_answer": 2.0}
{"source_row": 930, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {321: [(867, 2.0)], 867: [(45, 1.0)], 399: [(147, 2.0)], 147: [(45, 1.0)], 45: [(37, 3.0)], 37: [(985, 0.0)], 985: [], 521: [(321, 5.0), (399, 10.0)]}\nsource = 521\ntarget = 985\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 931, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {711: [(885, 2.0)], 168: [(180, 2.0)], 885: [(180, 2.0)], 180: [(736, 2.0)], 736: [(952, 1.0)], 952: [(909, 0.0)], 909: [], 570: [(711, 5.0), (168, 10.0)]}\nsource = 570\ntarget = 909\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 932, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {696: [(670, 10.0)], 651: [(670, 10.0)], 670: [(434, 2.0)], 434: [(306, 2.0)], 306: [(138, 0.0)], 138: [], 22: [(696, 30.0), (651, 10.0)]}\nsource = 22\ntarget = 138\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 44.0, "source_answer": 44.0}
{"source_row": 933, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {228: [(9, 2.0)], 9: [(130, 2.0), (469, 0.5)], 469: [(435, 0.0)], 130: [(435, 0.0)], 612: [(435, 0.0)], 435: [], 939: [(228, 10.0), (612, 2.0)]}\nsource = 939\ntarget = 435\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.0, "source_answer": 14.0}
{"source_row": 934, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {484: [(79, 20.0)], 79: [(652, 0.0)], 539: [(652, 0.0)], 290: [(652, 0.0)], 505: [(652, 0.0)], 353: [(652, 0.0)], 652: [], 929: [(484, 15.0), (539, 15.0), (290, 15.0), (505, 20.0), (353, 10.0)]}\nsource = 929\ntarget = 652\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 935, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {658: [(374, 15.0)], 309: [(374, 15.0)], 374: [(262, 1.0)], 262: [(543, 0.0)], 543: [], 977: [(658, 5.0), (309, 2.0)]}\nsource = 977\ntarget = 543\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 936, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {690: [(114, 0.0)], 962: [(752, 15.0)], 752: [(115, 2.0)], 115: [(114, 0.0)], 114: [], 692: [(690, 10.0), (962, 5.0)]}\nsource = 692\ntarget = 114\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 937, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {385: [(787, 10.0)], 787: [(334, 0.0)], 752: [(334, 0.0)], 334: [], 837: [(385, 30.0), (752, 60.0)]}\nsource = 837\ntarget = 334\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 938, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {591: [(152, 0.0)], 42: [(462, 1.0)], 462: [(355, 1.0)], 997: [(355, 1.0)], 355: [(57, 3.0)], 57: [(591, 5.0)], 152: [], 13: [(42, 1.0), (997, 1.0)]}\nsource = 13\ntarget = 152\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 939, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {640: [(320, 1.0)], 604: [(149, 1.0)], 768: [(866, 1.0)], 320: [(866, 1.0)], 149: [(866, 1.0)], 866: [(155, 0.0)], 155: [], 578: [(640, 1.0), (604, 1.0), (768, 2.0)]}\nsource = 578\ntarget = 155\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 940, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {503: [(982, 360.0)], 541: [(982, 360.0)], 251: [(982, 360.0)], 982: [(896, 120.0)], 896: [(596, 0.0)], 596: [], 926: [(503, 120.0), (541, 240.0), (251, 300.0)]}\nsource = 926\ntarget = 596\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 780.0, "source_answer": 780.0}
{"source_row": 941, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {800: [(347, 10.0)], 347: [(768, 5.0)], 768: [(224, 30.0)], 281: [(224, 30.0)], 224: [(236, 5.0)], 236: [(555, 0.0)], 555: [], 947: [(800, 10.0), (281, 15.0)]}\nsource = 947\ntarget = 555\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 942, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {181: [(515, 5.0), (548, 2.0), (489, 3.0)], 548: [(566, 10.0)], 489: [(566, 10.0)], 515: [(566, 10.0)], 566: [(649, 1.0)], 649: [(934, 0.0)], 934: [], 159: [(181, 5.0)]}\nsource = 159\ntarget = 934\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 943, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {630: [(155, 0.0)], 900: [(155, 0.0)], 55: [(155, 0.0)], 180: [(268, 300.0)], 950: [(155, 0.0)], 268: [(155, 0.0)], 155: [], 212: [(630, 120.0), (900, 60.0), (55, 300.0), (180, 120.0), (950, 12960.0)]}\nsource = 212\ntarget = 155\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12960.0, "source_answer": 12960.0}
{"source_row": 944, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {283: [(915, 3.0)], 915: [(694, 120.0)], 409: [(367, 2.0)], 367: [(694, 120.0)], 694: [(500, 2.0)], 500: [(163, 0.0)], 163: [], 548: [(283, 5.0), (409, 2.0)]}\nsource = 548\ntarget = 163\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130.0, "source_answer": 130.0}
{"source_row": 945, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {54: [(714, 30.0), (588, 45.0), (4, 30.0), (456, 60.0), (150, 60.0)], 4: [(833, 0.0)], 456: [(833, 0.0)], 714: [(833, 0.0)], 150: [(833, 0.0)], 588: [(833, 0.0)], 833: [], 728: [(54, 60.0)]}\nsource = 728\ntarget = 833\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 946, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {710: [(460, 10.0), (435, 15.0)], 460: [(178, 0.0)], 435: [(178, 0.0)], 645: [(178, 0.0)], 178: [], 801: [(710, 5.0), (645, 5.0)]}\nsource = 801\ntarget = 178\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 947, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {216: [(880, 0.17)], 652: [(565, 2.0)], 880: [(565, 2.0)], 565: [(397, 5.0)], 397: [(552, 3.0)], 552: [(132, 0.0)], 132: [], 832: [(216, 10.0), (652, 5.0)]}\nsource = 832\ntarget = 132\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.17, "source_answer": 20.17}
{"source_row": 948, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(193, 10.0)], 465: [(193, 10.0)], 193: [(727, 6.0)], 426: [(727, 6.0)], 727: [(124, 10.0)], 124: [(366, 0.0)], 366: [], 603: [(963, 5.0), (465, 2.0), (426, 3.0)]}\nsource = 603\ntarget = 366\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0}
{"source_row": 949, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {601: [(319, 120.0)], 560: [(944, 0.0)], 319: [(403, 1440.0)], 403: [(701, 240.0)], 701: [(944, 0.0)], 944: [], 716: [(601, 1440.0), (560, 120.0)]}\nsource = 716\ntarget = 944\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3240.0, "source_answer": 3240.0}
{"source_row": 950, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {800: [(606, 2.0)], 606: [(720, 5.0)], 720: [(332, 0.0)], 528: [(595, 2.0)], 595: [(332, 0.0)], 332: [], 186: [(800, 5.0), (528, 3.0)]}\nsource = 186\ntarget = 332\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 951, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {836: [(800, 2.0), (194, 3.0)], 234: [(800, 2.0), (194, 3.0)], 194: [(571, 1.0)], 800: [(571, 1.0)], 571: [(891, 0.0)], 891: [], 625: [(836, 1.0), (234, 2.0)]}\nsource = 625\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 952, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {275: [(75, 0.0)], 627: [(75, 0.0)], 807: [(75, 0.0)], 435: [(627, 2.0), (275, 5.0), (807, 10.0)], 75: [], 121: [(435, 1.0)]}\nsource = 121\ntarget = 75\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 953, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {706: [(280, 3.0)], 227: [(280, 3.0)], 750: [(280, 3.0)], 280: [(693, 1.0)], 693: [(908, 1.0)], 908: [(660, 0.0)], 660: [], 486: [(706, 5.0), (227, 5.0), (750, 3.0)]}\nsource = 486\ntarget = 660\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 954, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {570: [(412, 10.0)], 11: [(937, 4.0)], 201: [(937, 4.0)], 937: [(412, 10.0)], 612: [(412, 10.0)], 412: [(949, 0.0)], 949: [], 82: [(570, 5.0), (11, 3.0), (201, 5.0), (612, 2.0)]}\nsource = 82\ntarget = 949\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19.0, "source_answer": 19.0}
{"source_row": 955, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {935: [(592, 0.5)], 706: [(289, 0.5)], 592: [(289, 0.5)], 289: [(849, 1.0)], 849: [(615, 1.0)], 615: [(739, 0.0)], 739: [], 860: [(935, 2.0), (706, 1.0)]}\nsource = 860\ntarget = 739\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 956, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {192: [(290, 10.0)], 290: [(758, 10.0)], 999: [(758, 10.0)], 758: [(73, 240.0)], 73: [(322, 5.0)], 322: [(87, 0.0)], 87: [], 909: [(192, 20.0), (999, 5.0)]}\nsource = 909\ntarget = 87\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 285.0, "source_answer": 285.0}
{"source_row": 957, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {86: [(263, 10.0)], 263: [(673, 0.0)], 452: [(673, 0.0)], 673: [], 208: [(86, 30.0), (452, 120.0)]}\nsource = 208\ntarget = 673\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 958, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {981: [(291, 20.0)], 291: [(443, 2.0)], 490: [(868, 10.0)], 443: [(868, 10.0)], 868: [(775, 60.0)], 775: [(493, 0.0)], 493: [], 724: [(981, 20.0), (490, 5.0)]}\nsource = 724\ntarget = 493\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 112.0, "source_answer": 112.0}
{"source_row": 959, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {499: [(894, 120.0)], 287: [(894, 120.0)], 156: [(894, 120.0)], 894: [(745, 20160.0)], 45: [(845, 0.0)], 745: [(45, 20160.0)], 845: [], 821: [(499, 20160.0), (287, 20160.0), (156, 20160.0)]}\nsource = 821\ntarget = 845\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60600.0, "source_answer": 60600.0}
{"source_row": 960, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {789: [(710, 1.0)], 569: [(572, 1.0)], 572: [(179, 1.0)], 710: [(179, 1.0)], 179: [(981, 2.0)], 981: [(248, 0.0)], 248: [], 674: [(789, 1.0), (569, 1.0)]}\nsource = 674\ntarget = 248\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 961, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {19: [(65, 60.0)], 629: [(821, 10.0)], 821: [(65, 60.0)], 695: [(65, 60.0)], 114: [(65, 60.0)], 65: [(175, 0.0)], 175: [], 776: [(19, 60.0), (629, 15.0), (695, 30.0), (114, 20.0)]}\nsource = 776\ntarget = 175\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 962, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {662: [(803, 161280.0), (12, 259200.0)], 803: [(180, 0.0)], 12: [(180, 0.0)], 180: [], 67: [(662, 10080.0)]}\nsource = 67\ntarget = 180\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 269280.0, "source_answer": 269280.0}
{"source_row": 963, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {64: [(27, 3.0)], 901: [(27, 3.0)], 27: [(333, 1.0)], 333: [(403, 4.0)], 403: [(746, 1.0)], 746: [(190, 0.0)], 190: [], 40: [(64, 5.0), (901, 2.0)]}\nsource = 40\ntarget = 190\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.0, "source_answer": 14.0}
{"source_row": 964, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {166: [(981, 0.5)], 981: [(717, 1.0)], 855: [(599, 2.0)], 599: [(717, 1.0)], 499: [(232, 0.0)], 717: [(499, 2.0)], 232: [], 149: [(166, 1.0), (855, 5.0)]}\nsource = 149\ntarget = 232\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 965, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {467: [(194, 6.0)], 312: [(194, 6.0)], 194: [(743, 5.0)], 743: [(625, 0.0)], 625: [], 774: [(467, 5.0), (312, 3.0)]}\nsource = 774\ntarget = 625\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 966, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {205: [(632, 1.0)], 632: [(911, 5.0)], 911: [(630, 1.0)], 630: [(855, 60.0), (445, 180.0)], 445: [(554, 0.0)], 855: [(554, 0.0)], 554: [], 622: [(205, 5.0)]}\nsource = 622\ntarget = 554\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 192.0, "source_answer": 192.0}
{"source_row": 967, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {429: [(832, 120.0)], 832: [(826, 60.0), (835, 30.0)], 835: [(213, 0.0)], 826: [(783, 120.0)], 783: [(213, 0.0)], 627: [(213, 0.0)], 213: [], 489: [(429, 60.0), (627, 30.0)]}\nsource = 489\ntarget = 213\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 360.0, "source_answer": 360.0}
{"source_row": 968, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {628: [(194, 15.0)], 135: [(194, 15.0)], 194: [(821, 5.0)], 821: [(145, 15.0)], 145: [(673, 300.0)], 673: [(693, 0.0)], 693: [], 339: [(628, 10.0), (135, 5.0)]}\nsource = 339\ntarget = 693\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 345.0, "source_answer": 345.0}
{"source_row": 969, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {741: [(925, 0.0)], 525: [(741, 10.0)], 99: [(741, 10.0)], 713: [(741, 10.0)], 841: [(741, 10.0)], 360: [(741, 10.0)], 925: [], 27: [(525, 5.0), (99, 7.0), (713, 3.0), (841, 5.0), (360, 4.0)]}\nsource = 27\ntarget = 925\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 970, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {334: [(953, 180.0)], 953: [(564, 0.0)], 488: [(564, 0.0)], 583: [(564, 0.0)], 564: [], 879: [(334, 120.0), (488, 120.0), (583, 60.0)]}\nsource = 879\ntarget = 564\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 971, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {347: [(495, 10.0)], 495: [(396, 5.0)], 396: [(869, 15.0)], 681: [(869, 15.0)], 869: [(832, 10.0)], 832: [(247, 0.0)], 247: [], 722: [(347, 10.0), (681, 2.0)]}\nsource = 722\ntarget = 247\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 972, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {639: [(234, 1.0)], 685: [(234, 1.0)], 234: [(759, 4.0)], 759: [(321, 1.0)], 321: [(249, 0.0)], 249: [], 598: [(639, 5.0), (685, 1.0)]}\nsource = 598\ntarget = 249\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 973, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {543: [(427, 4.0)], 454: [(427, 4.0)], 669: [(427, 4.0)], 427: [(253, 2.0)], 253: [(135, 1.0)], 135: [(488, 0.0)], 488: [], 559: [(543, 5.0), (454, 3.0), (669, 2.0)]}\nsource = 559\ntarget = 488\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 974, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {250: [(935, 2.0), (787, 2.0)], 935: [(898, 5.0)], 787: [(898, 5.0)], 898: [(178, 5.0)], 178: [(308, 60.0)], 308: [(159, 0.0)], 159: [], 551: [(250, 5.0)]}\nsource = 551\ntarget = 159\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 77.0, "source_answer": 77.0}
{"source_row": 975, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {736: [(528, 5.0)], 135: [(528, 5.0)], 596: [(528, 5.0)], 655: [(528, 5.0)], 47: [(528, 5.0)], 528: [(422, 0.0)], 422: [], 109: [(736, 2.0), (135, 2.0), (596, 2.0), (655, 2.0), (47, 2.0)]}\nsource = 109\ntarget = 422\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 976, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {512: [(325, 0.0)], 322: [(325, 0.0)], 293: [(325, 0.0)], 700: [(325, 0.0)], 639: [(325, 0.0)], 752: [(325, 0.0)], 915: [(700, 60.0)], 325: [], 586: [(512, 15.0), (322, 30.0), (293, 60.0), (639, 5.0), (752, 60.0), (915, 120.0)]}\nsource = 586\ntarget = 325\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 977, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {57: [(82, 5.0)], 82: [(135, 5.0)], 451: [(135, 5.0)], 135: [(322, 20.0)], 688: [(322, 20.0)], 322: [(467, 60.0)], 467: [(881, 0.0)], 881: [], 620: [(57, 45.0), (451, 10.0), (688, 15.0)]}\nsource = 620\ntarget = 881\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 978, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {447: [(87, 5.0)], 87: [(922, 120.0)], 922: [(926, 5.0)], 179: [(501, 0.0)], 926: [(501, 0.0)], 501: [], 166: [(447, 10.0), (179, 15.0)]}\nsource = 166\ntarget = 501\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 140.0, "source_answer": 140.0}
{"source_row": 979, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {16: [(342, 5.0)], 263: [(342, 5.0)], 342: [(657, 2.0)], 657: [(281, 20160.0)], 281: [(179, 5.0)], 179: [(178, 1.0)], 178: [(862, 0.0)], 862: [], 827: [(16, 10.0), (263, 10.0)]}\nsource = 827\ntarget = 862\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20183.0, "source_answer": 20183.0}
{"source_row": 980, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {665: [(329, 45.0)], 475: [(523, 0.0)], 324: [(523, 0.0)], 329: [(523, 0.0)], 443: [(523, 0.0)], 523: [], 418: [(665, 30.0), (475, 60.0), (324, 20.0), (443, 60.0)]}\nsource = 418\ntarget = 523\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 981, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {48: [(505, 1.0), (83, 1.0), (230, 1.0)], 83: [(393, 0.0)], 230: [(393, 0.0)], 505: [(393, 0.0)], 393: [], 97: [(48, 15.0)]}\nsource = 97\ntarget = 393\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0}
{"source_row": 982, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {518: [(395, 2.0)], 49: [(395, 2.0)], 692: [(395, 2.0)], 395: [(899, 1440.0)], 899: [(755, 0.0)], 755: [], 611: [(518, 10.0), (49, 10.0), (692, 10.0)]}\nsource = 611\ntarget = 755\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1452.0, "source_answer": 1452.0}
{"source_row": 983, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {708: [(154, 15.0)], 154: [(911, 5.0)], 911: [(935, 10.0)], 935: [(927, 300.0)], 464: [(927, 300.0)], 927: [(754, 120.0)], 754: [(48, 0.0)], 48: [], 168: [(708, 10.0), (464, 10.0)]}\nsource = 168\ntarget = 48\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 460.0, "source_answer": 460.0}
{"source_row": 984, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {442: [(851, 1.0)], 851: [(84, 3.0)], 880: [(84, 3.0)], 84: [(491, 1.0), (113, 1.0)], 113: [(304, 0.0)], 491: [(304, 0.0)], 304: [], 119: [(442, 2.0), (880, 1.0)]}\nsource = 119\ntarget = 304\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 985, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {307: [(510, 5.0)], 51: [(678, 20.0)], 678: [(152, 10.0)], 510: [(152, 10.0)], 152: [(549, 5.0)], 549: [(942, 25.0)], 942: [(248, 0.0)], 248: [], 280: [(307, 10.0), (51, 15.0)]}\nsource = 280\ntarget = 248\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 986, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {824: [(701, 5.0)], 701: [(839, 10.0)], 839: [(672, 1440.0)], 672: [(538, 5.0), (91, 2.0)], 188: [(994, 0.0)], 538: [(188, 15.0)], 91: [(994, 0.0)], 994: [], 716: [(824, 5.0)]}\nsource = 716\ntarget = 994\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1480.0, "source_answer": 1480.0}
{"source_row": 987, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {376: [(918, 2.0)], 769: [(324, 3.0)], 324: [(284, 1.0)], 284: [(918, 2.0)], 918: [(639, 15.0)], 639: [(163, 5.0)], 163: [(859, 0.0)], 859: [], 166: [(376, 5.0), (769, 2.0)]}\nsource = 166\ntarget = 859\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0}
{"source_row": 988, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {309: [(507, 2.0)], 507: [(77, 0.0)], 589: [(77, 0.0)], 77: [], 687: [(309, 5.0), (589, 2.0)]}\nsource = 687\ntarget = 77\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 989, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {1: [(867, 5.0)], 867: [(451, 10.0)], 451: [(885, 5.0)], 860: [(793, 10.0)], 885: [(922, 2.0)], 793: [(885, 5.0)], 922: [(125, 0.0)], 125: [], 913: [(1, 5.0), (860, 15.0)]}\nsource = 913\ntarget = 125\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 990, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {191: [(842, 3.0)], 130: [(195, 3.0)], 195: [(842, 3.0)], 842: [(2, 3.0)], 2: [(487, 25.0)], 487: [(799, 5.0)], 799: [(368, 0.0)], 368: [], 22: [(191, 10.0), (130, 15.0)]}\nsource = 22\ntarget = 368\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 54.0, "source_answer": 54.0}
{"source_row": 991, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {889: [(460, 0.0)], 317: [(460, 0.0)], 529: [(915, 3.0)], 915: [(460, 0.0)], 802: [(460, 0.0)], 408: [(460, 0.0)], 460: [], 572: [(889, 20.0), (317, 10.0), (529, 2.0), (802, 5.0), (408, 2.0)]}\nsource = 572\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 992, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {447: [(673, 30.0), (847, 120.0)], 673: [(188, 0.0)], 847: [(188, 0.0)], 849: [(188, 0.0)], 250: [(188, 0.0)], 885: [(188, 0.0)], 752: [(849, 30.0), (885, 120.0), (447, 120.0), (250, 60.0)], 188: [], 308: [(752, 120.0)]}\nsource = 308\ntarget = 188\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 360.0, "source_answer": 360.0}
{"source_row": 993, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {606: [(579, 3.0), (3, 3.0)], 256: [(579, 3.0), (3, 3.0)], 579: [(747, 2.0)], 747: [(166, 2.0)], 3: [(166, 2.0)], 166: [(483, 1.0)], 483: [(318, 0.0)], 318: [], 707: [(606, 2.0), (256, 1.0)]}\nsource = 707\ntarget = 318\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 994, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {665: [(156, 5.0)], 237: [(156, 5.0)], 580: [(156, 5.0)], 696: [(156, 5.0)], 156: [(645, 0.0)], 645: [], 433: [(665, 1.0), (237, 1.0), (580, 1.0), (696, 1.0)]}\nsource = 433\ntarget = 645\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 995, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {44: [(697, 2.0)], 494: [(946, 0.5)], 946: [(697, 2.0)], 697: [(644, 1.0)], 644: [(508, 0.0)], 508: [], 758: [(44, 3.0), (494, 1.0)]}\nsource = 758\ntarget = 508\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 996, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {260: [(470, 15.0)], 32: [(470, 15.0)], 470: [(542, 0.0)], 542: [], 624: [(260, 5.0), (32, 10.0)]}\nsource = 624\ntarget = 542\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 997, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {398: [(534, 30.0)], 319: [(838, 5.0)], 838: [(534, 30.0)], 534: [(924, 30.0)], 924: [(699, 120.0)], 699: [(805, 30.0)], 805: [(991, 0.0)], 991: [], 292: [(398, 5.0), (319, 5.0)]}\nsource = 292\ntarget = 991\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 220.0, "source_answer": 220.0}
{"source_row": 998, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {928: [(463, 0.0)], 662: [(296, 3.0)], 296: [(463, 0.0)], 463: [], 851: [(928, 5.0), (662, 10.0)]}\nsource = 851\ntarget = 463\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 999, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {846: [(300, 45.0), (395, 10.0), (716, 20.0), (934, 10.0), (100, 30.0), (124, 5.0)], 934: [(208, 0.0)], 100: [(208, 0.0)], 395: [(208, 0.0)], 124: [(208, 0.0)], 716: [(208, 0.0)], 300: [(208, 0.0)], 208: [], 388: [(846, 15.0)]}\nsource = 388\ntarget = 208\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1000, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {546: [(608, 1.0)], 719: [(608, 1.0)], 608: [(830, 0.0)], 238: [(294, 1.0)], 294: [(830, 0.0)], 164: [(881, 2.0)], 881: [(830, 0.0)], 830: [], 223: [(546, 2.0), (719, 1.0), (238, 3.0), (164, 1.0)]}\nsource = 223\ntarget = 830\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1001, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {553: [(607, 0.0)], 136: [(990, 5.0)], 990: [(607, 0.0)], 476: [(607, 0.0)], 829: [(607, 0.0)], 607: [], 858: [(553, 5.0), (136, 10.0), (476, 15.0), (829, 10.0)]}\nsource = 858\ntarget = 607\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1002, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {907: [(160, 10.0)], 656: [(160, 10.0)], 258: [(748, 3.0)], 748: [(540, 5.0)], 540: [(160, 10.0)], 160: [(276, 2.0)], 276: [(154, 0.0)], 154: [], 522: [(907, 10.0), (656, 10.0), (258, 3.0)]}\nsource = 522\ntarget = 154\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.0, "source_answer": 23.0}
{"source_row": 1003, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {165: [(539, 30.0)], 352: [(921, 20160.0), (165, 30.0)], 975: [(775, 0.0)], 921: [(775, 0.0)], 539: [(189, 120.0), (975, 20160.0)], 189: [(775, 0.0)], 775: [], 514: [(352, 15.0)]}\nsource = 514\ntarget = 775\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20235.0, "source_answer": 20235.0}
{"source_row": 1004, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {841: [(942, 0.0)], 578: [(458, 4320.0), (841, 60.0)], 458: [(942, 0.0)], 942: [], 303: [(578, 1440.0)]}\nsource = 303\ntarget = 942\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5760.0, "source_answer": 5760.0}
{"source_row": 1005, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {367: [(829, 5.0)], 490: [(959, 1.0)], 959: [(829, 5.0)], 829: [(686, 5.0)], 686: [(401, 0.0)], 401: [], 120: [(367, 5.0), (490, 3.0)]}\nsource = 120\ntarget = 401\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1006, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {487: [(593, 120960.0)], 675: [(417, 0.0)], 593: [(417, 0.0)], 417: [], 194: [(487, 1440.0), (675, 20160.0)]}\nsource = 194\ntarget = 417\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 122400.0, "source_answer": 122400.0}
{"source_row": 1007, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {284: [(928, 3.0)], 254: [(928, 3.0)], 928: [(37, 1.0)], 37: [(276, 3.0)], 276: [(776, 10.0)], 776: [(871, 20.0)], 871: [(80, 0.0)], 80: [], 170: [(284, 5.0), (254, 5.0)]}\nsource = 170\ntarget = 80\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 42.0, "source_answer": 42.0}
{"source_row": 1008, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {271: [(76, 2.0)], 980: [(76, 2.0)], 76: [(752, 1.0)], 752: [(585, 0.0)], 585: [], 570: [(271, 5.0), (980, 3.0)]}\nsource = 570\ntarget = 585\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 1009, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {683: [(365, 5.0)], 621: [(365, 5.0)], 703: [(365, 5.0)], 365: [(391, 0.0)], 391: [], 206: [(683, 5.0), (621, 10.0), (703, 15.0)]}\nsource = 206\ntarget = 391\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1010, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {390: [(580, 60.0)], 794: [(580, 60.0)], 580: [(0, 0.0)], 0: [], 618: [(390, 10.0), (794, 10.0)]}\nsource = 618\ntarget = 0\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 1011, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {274: [(47, 2880.0), (530, 20160.0)], 263: [(530, 20160.0)], 530: [(894, 0.0)], 47: [(894, 0.0)], 894: [], 0: [(274, 2880.0), (263, 4320.0)]}\nsource = 0\ntarget = 894\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24480.0, "source_answer": 24480.0}
{"source_row": 1012, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {21: [(230, 60.0)], 230: [(451, 0.0)], 961: [(230, 60.0)], 451: [], 377: [(21, 5.0), (961, 1.0)]}\nsource = 377\ntarget = 451\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 1013, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {245: [(301, 15.0)], 250: [(88, 0.0)], 301: [(88, 0.0)], 88: [], 549: [(245, 5.0), (250, 10.0)]}\nsource = 549\ntarget = 88\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1014, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {767: [(889, 5.0)], 889: [(826, 60.0)], 826: [(460, 10.0), (477, 60.0)], 460: [(404, 15.0)], 659: [(404, 15.0)], 404: [(262, 0.0)], 477: [(659, 5.0)], 262: [], 561: [(767, 10.0)]}\nsource = 561\ntarget = 262\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 155.0, "source_answer": 155.0}
{"source_row": 1015, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {164: [(793, 30.0)], 509: [(94, 4.0)], 94: [(311, 5.0)], 311: [(793, 30.0)], 793: [(303, 30.0)], 303: [(116, 0.0)], 116: [], 710: [(164, 10.0), (509, 10.0)]}\nsource = 710\ntarget = 116\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 79.0, "source_answer": 79.0}
{"source_row": 1016, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {676: [(586, 45.0), (644, 30.0), (121, 60.0)], 644: [(22, 0.0)], 121: [(22, 0.0)], 586: [(22, 0.0)], 22: [], 719: [(676, 60.0)]}\nsource = 719\ntarget = 22\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 1017, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {996: [(223, 0.0)], 988: [(414, 15.0)], 414: [(223, 0.0)], 223: [], 23: [(996, 10.0), (988, 20.0)]}\nsource = 23\ntarget = 223\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1018, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {175: [(436, 1.0)], 436: [(871, 5.0)], 871: [(178, 0.0)], 849: [(178, 0.0)], 178: [], 78: [(175, 2.0), (849, 2.0)]}\nsource = 78\ntarget = 178\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 1019, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {788: [(629, 10080.0)], 629: [(834, 0.0)], 333: [(834, 0.0)], 94: [(834, 0.0)], 974: [(834, 0.0)], 577: [(834, 0.0)], 910: [(834, 0.0)], 834: [], 340: [(788, 1440.0), (333, 10080.0), (94, 1440.0), (974, 10080.0), (577, 10080.0), (910, 10080.0)]}\nsource = 340\ntarget = 834\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11520.0, "source_answer": 11520.0}
{"source_row": 1020, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {0: [(400, 10.0), (640, 10.0), (777, 15.0), (297, 10.0), (193, 15.0), (67, 10.0)], 297: [(708, 0.0)], 193: [(708, 0.0)], 640: [(708, 0.0)], 67: [(708, 0.0)], 777: [(708, 0.0)], 400: [(708, 0.0)], 708: [], 930: [(0, 30.0)]}\nsource = 930\ntarget = 708\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1021, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {788: [(692, 3.0)], 113: [(692, 3.0)], 692: [(726, 1.0)], 726: [(854, 0.0)], 854: [], 352: [(788, 5.0), (113, 3.0)]}\nsource = 352\ntarget = 854\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1022, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {338: [(610, 2.0)], 610: [(170, 3.0)], 375: [(170, 3.0)], 170: [(517, 7.0)], 540: [(517, 7.0)], 517: [(764, 7.0)], 764: [(458, 1.0)], 458: [(13, 0.0)], 13: [], 936: [(338, 15.0), (375, 2.0), (540, 1.0)]}\nsource = 936\ntarget = 13\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1023, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {648: [(886, 180.0)], 886: [(146, 420.0)], 146: [(554, 0.0)], 751: [(554, 0.0)], 554: [], 249: [(648, 120.0), (751, 120.0)]}\nsource = 249\ntarget = 554\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 720.0, "source_answer": 720.0}
{"source_row": 1024, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {509: [(640, 0.0)], 14: [(640, 0.0)], 735: [(640, 0.0)], 809: [(640, 0.0)], 719: [(14, 60.0)], 640: [], 524: [(509, 30.0), (735, 20.0), (809, 10.0), (719, 45.0)]}\nsource = 524\ntarget = 640\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 1025, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {384: [(617, 10.0)], 617: [(185, 0.0)], 135: [(185, 0.0)], 185: [], 945: [(384, 1440.0), (135, 60.0)]}\nsource = 945\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1450.0, "source_answer": 1450.0}
{"source_row": 1026, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {805: [(155, 10.0)], 31: [(155, 10.0)], 155: [(49, 15.0)], 49: [(640, 30.0)], 826: [(996, 0.0)], 640: [(826, 5.0)], 996: [], 444: [(805, 15.0), (31, 5.0)]}\nsource = 444\ntarget = 996\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 1027, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {571: [(11, 5.0), (407, 1.0), (811, 10.0), (713, 2.0)], 811: [(834, 0.0)], 713: [(834, 0.0)], 11: [(834, 0.0)], 407: [(834, 0.0)], 834: [], 147: [(571, 5.0)]}\nsource = 147\ntarget = 834\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1028, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {454: [(967, 12.0), (16, 10.0)], 16: [(625, 15.0)], 625: [(159, 5.0)], 967: [(373, 3.0)], 159: [(71, 0.0)], 373: [(71, 0.0)], 71: [], 643: [(454, 5.0)]}\nsource = 643\ntarget = 71\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1029, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {829: [(298, 300.0)], 329: [(829, 60.0)], 298: [(234, 0.0)], 503: [(234, 0.0)], 234: [], 890: [(329, 120.0), (503, 30.0)]}\nsource = 890\ntarget = 234\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 480.0, "source_answer": 480.0}
{"source_row": 1030, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {634: [(665, 5.0)], 263: [(665, 5.0)], 665: [(997, 0.0)], 818: [(263, 5.0)], 997: [], 115: [(634, 2.0), (818, 3.0)]}\nsource = 115\ntarget = 997\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 1031, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {265: [(284, 5.0)], 210: [(284, 5.0)], 284: [(149, 0.0)], 149: [], 163: [(265, 10.0), (210, 5.0)]}\nsource = 163\ntarget = 149\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1032, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {997: [(629, 2.0)], 629: [(486, 15.0)], 929: [(437, 5.0)], 437: [(486, 15.0)], 486: [(281, 30.0)], 281: [(495, 5.0)], 495: [(500, 10.0)], 500: [(836, 0.0)], 836: [], 669: [(997, 5.0), (929, 10.0)]}\nsource = 669\ntarget = 836\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 1033, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {516: [(474, 10.0)], 474: [(969, 15.0)], 969: [(57, 15.0)], 57: [(462, 5.0)], 194: [(462, 5.0)], 462: [(918, 5.0)], 918: [(989, 0.0)], 989: [], 489: [(516, 5.0), (194, 5.0)]}\nsource = 489\ntarget = 989\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1034, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {0: [(677, 5.0)], 677: [(208, 0.0)], 543: [(208, 0.0)], 208: [], 936: [(0, 2.0), (543, 2.0)]}\nsource = 936\ntarget = 208\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1035, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {116: [(471, 60.0), (573, 10.0)], 573: [(211, 15.0)], 375: [(357, 0.0)], 471: [(211, 15.0)], 211: [(375, 20.0)], 357: [], 665: [(116, 5.0)]}\nsource = 665\ntarget = 357\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0}
{"source_row": 1036, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(425, 0.25)], 139: [(425, 0.25)], 425: [(543, 0.25)], 543: [(225, 2.0)], 225: [(899, 0.0)], 899: [], 683: [(505, 1.0), (139, 0.5)]}\nsource = 683\ntarget = 899\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.5, "source_answer": 3.5}
{"source_row": 1037, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(995, 2.0)], 995: [(76, 2.0), (443, 2.0), (156, 2.0), (109, 2.0)], 156: [(23, 3.0)], 76: [(23, 3.0)], 109: [(23, 3.0)], 443: [(23, 3.0)], 23: [(680, 0.0)], 680: [], 944: [(863, 5.0)]}\nsource = 944\ntarget = 680\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 1038, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {108: [(700, 10.0)], 875: [(449, 8.0)], 449: [(864, 3.0)], 864: [(700, 10.0)], 700: [(673, 45.0)], 673: [(556, 120.0)], 556: [(158, 2.0)], 158: [(201, 0.0)], 201: [], 377: [(108, 5.0), (875, 5.0)]}\nsource = 377\ntarget = 201\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 193.0, "source_answer": 193.0}
{"source_row": 1039, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {267: [(212, 20160.0), (485, 1440.0)], 212: [(342, 0.0)], 485: [(342, 0.0)], 306: [(342, 0.0)], 342: [], 1: [(267, 86400.0), (306, 20160.0)]}\nsource = 1\ntarget = 342\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 106560.0, "source_answer": 106560.0}
{"source_row": 1040, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {411: [(43, 10.0)], 43: [(267, 3.0)], 267: [(628, 0.0)], 344: [(628, 0.0)], 628: [], 864: [(411, 5.0), (344, 5.0)]}\nsource = 864\ntarget = 628\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 1041, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {601: [(552, 3.0)], 552: [(851, 1.0)], 200: [(851, 1.0)], 851: [(735, 3.0)], 735: [(520, 2.0)], 520: [(375, 20.0)], 375: [(955, 5.0)], 955: [(193, 0.0)], 193: [], 513: [(601, 2.0), (200, 2.0)]}\nsource = 513\ntarget = 193\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.0, "source_answer": 36.0}
{"source_row": 1042, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {662: [(362, 0.0)], 597: [(621, 2.0)], 621: [(786, 5.0)], 786: [(31, 1.0)], 31: [(642, 5.0)], 268: [(769, 1.0)], 642: [(268, 10.0)], 769: [(362, 0.0)], 362: [], 344: [(662, 30.0), (597, 1.0)]}\nsource = 344\ntarget = 362\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1043, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {420: [(577, 40.0)], 22: [(906, 4.0)], 105: [(906, 4.0)], 906: [(577, 40.0)], 577: [(762, 10.0)], 762: [(310, 10.0)], 310: [(119, 1.0)], 119: [(359, 0.0)], 359: [], 526: [(420, 2.0), (22, 5.0), (105, 3.0)]}\nsource = 526\ntarget = 359\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 1044, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {97: [(782, 10.0), (886, 5.0)], 782: [(174, 0.0)], 886: [(174, 0.0)], 842: [(874, 30.0)], 874: [(174, 0.0)], 174: [], 928: [(97, 10.0), (842, 2.0)]}\nsource = 928\ntarget = 174\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 1045, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {181: [(903, 15.0)], 159: [(290, 240.0)], 903: [(476, 0.0)], 290: [(903, 15.0)], 476: [], 945: [(181, 10.0), (159, 5.0)]}\nsource = 945\ntarget = 476\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 260.0, "source_answer": 260.0}
{"source_row": 1046, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {449: [(434, 0.0)], 440: [(449, 10.0)], 151: [(627, 10.0)], 627: [(434, 0.0)], 413: [(171, 10.0)], 171: [(434, 0.0)], 602: [(434, 0.0)], 884: [(602, 8.0)], 434: [], 24: [(440, 15.0), (151, 5.0), (413, 5.0), (884, 10.0)]}\nsource = 24\ntarget = 434\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1047, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {632: [(853, 2.0)], 853: [(185, 3.0)], 635: [(185, 3.0)], 185: [(679, 1.0)], 679: [(920, 4.0)], 920: [(226, 10.0)], 226: [(248, 2.0)], 248: [(9, 0.0)], 9: [], 264: [(632, 5.0), (635, 2.0)]}\nsource = 264\ntarget = 9\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0}
{"source_row": 1048, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {750: [(422, 3.0), (990, 3.0), (243, 3.0), (401, 3.0), (75, 2.0), (167, 3.0), (56, 4.0)], 167: [(86, 0.0)], 401: [(86, 0.0)], 243: [(86, 0.0)], 56: [(86, 0.0)], 75: [(86, 0.0)], 422: [(86, 0.0)], 990: [(86, 0.0)], 86: [], 33: [(750, 5.0)]}\nsource = 33\ntarget = 86\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1049, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {425: [(699, 10.0)], 699: [(943, 2.0)], 712: [(943, 2.0)], 943: [(492, 60.0)], 492: [(690, 10.0)], 690: [(405, 0.0)], 405: [], 403: [(425, 60.0), (712, 5.0)]}\nsource = 403\ntarget = 405\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 142.0, "source_answer": 142.0}
{"source_row": 1050, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {987: [(794, 2.0)], 981: [(794, 2.0)], 599: [(794, 2.0)], 294: [(794, 2.0)], 161: [(794, 2.0)], 794: [(748, 1.0)], 748: [(138, 30.0)], 138: [(86, 0.0)], 86: [], 711: [(987, 10.0), (981, 5.0), (599, 3.0), (294, 5.0), (161, 5.0)]}\nsource = 711\ntarget = 86\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43.0, "source_answer": 43.0}
{"source_row": 1051, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {225: [(139, 0.0)], 527: [(244, 5.0), (225, 5.0)], 244: [(139, 0.0)], 139: [], 853: [(527, 10.0)]}\nsource = 853\ntarget = 139\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1052, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {951: [(965, 3.0)], 2: [(514, 5.0)], 514: [(965, 3.0)], 965: [(404, 2.0)], 404: [(368, 10.0)], 368: [(142, 2.0)], 142: [(206, 1.0)], 206: [(767, 0.0)], 767: [], 387: [(951, 1.0), (2, 2.0)]}\nsource = 387\ntarget = 767\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1053, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {167: [(464, 30.0)], 464: [(115, 20.0)], 115: [(198, 0.0)], 248: [(198, 0.0)], 416: [(198, 0.0)], 198: [], 285: [(167, 5.0), (248, 60.0), (416, 60.0)]}\nsource = 285\ntarget = 198\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1054, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {638: [(919, 2.0)], 886: [(919, 2.0)], 159: [(919, 2.0)], 919: [(958, 0.0)], 365: [(919, 2.0)], 667: [(919, 2.0)], 467: [(919, 2.0)], 958: [(273, 0.0)], 273: [], 502: [(638, 2.0), (886, 1.0), (159, 1.0), (365, 1.0), (667, 1.0), (467, 1.0)]}\nsource = 502\ntarget = 273\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1055, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {17: [(371, 1.0)], 371: [(849, 10.0)], 849: [(433, 30.0)], 433: [(166, 2.0)], 166: [(711, 10.0)], 711: [(920, 10.0)], 834: [(920, 10.0)], 920: [(736, 2.0)], 736: [(322, 0.0)], 322: [], 267: [(17, 5.0), (834, 5.0)]}\nsource = 267\ntarget = 322\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 1056, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {408: [(532, 2.0)], 186: [(532, 2.0)], 446: [(532, 2.0)], 532: [(444, 10.0)], 444: [(487, 2.0)], 487: [(679, 30.0)], 679: [(639, 5.0)], 639: [(903, 1.0)], 903: [(608, 0.0)], 608: [], 818: [(408, 5.0), (186, 2.0), (446, 10.0)]}\nsource = 818\ntarget = 608\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1057, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {79: [(186, 10.0)], 186: [(551, 15.0)], 551: [(424, 10.0), (770, 5.0)], 424: [(859, 10.0)], 770: [(521, 5.0)], 521: [(859, 10.0)], 859: [(468, 5.0)], 468: [(633, 2.0)], 633: [(588, 0.0)], 588: [], 168: [(79, 5.0)]}\nsource = 168\ntarget = 588\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 57.0, "source_answer": 57.0}
{"source_row": 1058, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {906: [(384, 2.0)], 969: [(576, 2.0)], 384: [(961, 1.0)], 961: [(576, 2.0)], 125: [(412, 0.0)], 576: [(125, 3.0)], 412: [], 652: [(906, 5.0), (969, 2.0)]}\nsource = 652\ntarget = 412\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 1059, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(597, 20.0)], 624: [(377, 5.0)], 377: [(832, 3.0)], 832: [(649, 5.0)], 649: [(348, 2.0)], 348: [(597, 20.0)], 597: [(67, 10.0)], 67: [(288, 0.0)], 288: [(552, 0.0)], 552: [], 536: [(881, 10.0), (624, 5.0)]}\nsource = 536\ntarget = 552\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1060, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {913: [(217, 2.0), (721, 2.0), (711, 2.0)], 217: [(905, 3.0)], 711: [(905, 3.0)], 721: [(905, 3.0)], 905: [(893, 15.0), (265, 10.0), (778, 15.0), (878, 10.0)], 893: [(43, 0.0)], 878: [(43, 0.0)], 265: [(43, 0.0)], 778: [(43, 0.0)], 43: [], 658: [(913, 5.0)]}\nsource = 658\ntarget = 43\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1061, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {298: [(515, 15.0), (816, 30.0), (698, 20.0), (831, 30.0)], 698: [(530, 0.0)], 831: [(530, 0.0)], 515: [(530, 0.0)], 816: [(530, 0.0)], 530: [], 968: [(298, 10.0)]}\nsource = 968\ntarget = 530\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 1062, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(444, 1.0)], 444: [(703, 1.0)], 703: [(307, 0.5)], 307: [(457, 0.5)], 457: [(60, 0.0)], 665: [(881, 1.0)], 88: [(881, 1.0)], 293: [(881, 1.0)], 47: [(881, 1.0)], 60: [], 290: [(665, 1.0), (88, 1.0), (293, 1.0), (47, 2.0)]}\nsource = 290\ntarget = 60\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 1063, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {273: [(686, 1.0)], 686: [(841, 0.17)], 181: [(177, 0.17)], 177: [(841, 0.17)], 841: [(287, 0.25)], 287: [(582, 15.0)], 582: [(995, 1.0)], 995: [(204, 2.0)], 204: [(178, 0.0)], 178: [], 56: [(273, 2.0), (181, 0.5)]}\nsource = 56\ntarget = 178\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.42, "source_answer": 21.42}
{"source_row": 1064, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {12: [(479, 1.0)], 278: [(479, 1.0)], 479: [(450, 0.0)], 586: [(450, 0.0)], 926: [(450, 0.0)], 598: [(450, 0.0)], 552: [(450, 0.0)], 460: [(450, 0.0)], 406: [(450, 0.0)], 450: [], 603: [(12, 1.0), (278, 1.0), (586, 1.0), (926, 1.0), (598, 1.0), (552, 1.0), (460, 1.0), (406, 1.0)]}\nsource = 603\ntarget = 450\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.0, "source_answer": 2.0}
{"source_row": 1065, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {241: [(240, 20.0)], 543: [(60, 5.0)], 60: [(328, 2.0)], 328: [(376, 2.0)], 376: [(171, 1.0)], 171: [(582, 1.0)], 582: [(240, 20.0)], 240: [(536, 2.0)], 536: [(800, 0.0)], 800: [], 994: [(241, 5.0), (543, 2.0)]}\nsource = 994\ntarget = 800\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1066, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {735: [(317, 0.0)], 513: [(317, 0.0)], 552: [(735, 30.0)], 317: [], 974: [(513, 20.0), (552, 45.0)]}\nsource = 974\ntarget = 317\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 1067, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {55: [(531, 0.0)], 260: [(55, 5.0)], 292: [(531, 0.0)], 264: [(292, 10.0)], 531: [], 850: [(260, 2.0), (264, 2.0)]}\nsource = 850\ntarget = 531\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 1068, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {207: [(816, 0.05)], 816: [(196, 0.0)], 519: [(702, 0.07)], 762: [(979, 0.03)], 230: [(196, 0.0)], 636: [(979, 0.03)], 979: [(702, 0.07)], 702: [(196, 0.0)], 196: [], 763: [(207, 0.08), (519, 0.02), (762, 0.03), (230, 0.03), (636, 0.05)]}\nsource = 763\ntarget = 196\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.15, "source_answer": 0.15}
{"source_row": 1069, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {124: [(913, 1.0)], 624: [(14, 3.0)], 14: [(297, 2.0)], 619: [(297, 2.0)], 297: [(140, 3.0)], 913: [(746, 2.0)], 140: [(913, 1.0)], 746: [(647, 2.0)], 647: [(475, 0.0)], 475: [], 557: [(124, 2.0), (624, 5.0), (619, 2.0)]}\nsource = 557\ntarget = 475\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 1070, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {139: [(99, 10.0)], 736: [(385, 5.0)], 385: [(34, 2.0)], 34: [(381, 20.0)], 99: [(34, 2.0)], 381: [(218, 2.0)], 218: [(404, 3.0)], 92: [(404, 3.0)], 404: [(912, 0.0)], 912: [], 228: [(139, 5.0), (736, 10.0), (92, 5.0)]}\nsource = 228\ntarget = 912\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 42.0, "source_answer": 42.0}
{"source_row": 1071, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {787: [(664, 3.0)], 664: [(814, 0.0)], 590: [(814, 0.0)], 355: [(787, 5.0)], 814: [], 970: [(590, 2.0), (355, 1.0)]}\nsource = 970\ntarget = 814\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1072, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {597: [(550, 15.0)], 850: [(550, 15.0)], 550: [(513, 10.0), (43, 30.0)], 513: [(584, 15.0)], 43: [(870, 15.0)], 870: [(584, 15.0)], 506: [(148, 10.0)], 584: [(506, 10.0)], 148: [(263, 0.0)], 263: [], 295: [(597, 10.0), (850, 20.0)]}\nsource = 295\ntarget = 263\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 115.0, "source_answer": 115.0}
{"source_row": 1073, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {989: [(444, 2.0)], 260: [(444, 2.0)], 591: [(444, 2.0)], 444: [(815, 3.0)], 815: [(519, 5.0)], 519: [(721, 8.0)], 85: [(721, 8.0)], 721: [(435, 1.0)], 435: [(207, 0.0)], 207: [], 856: [(989, 1.0), (260, 2.0), (591, 3.0), (85, 2.0)]}\nsource = 856\ntarget = 207\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 1074, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {573: [(941, 3.0)], 448: [(941, 3.0)], 941: [(539, 1.0)], 539: [(475, 0.5)], 475: [(728, 0.0)], 728: [], 524: [(573, 1.0), (448, 2.0)]}\nsource = 524\ntarget = 728\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.5, "source_answer": 6.5}
{"source_row": 1075, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {688: [(525, 10.0)], 525: [(412, 15.0)], 453: [(412, 15.0)], 412: [(990, 30.0)], 990: [(110, 0.0)], 110: [], 296: [(688, 10.0), (453, 2.0)]}\nsource = 296\ntarget = 110\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 1076, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {688: [(31, 3.0)], 550: [(98, 0.0)], 982: [(567, 2.0)], 567: [(31, 3.0)], 31: [(694, 2.0)], 694: [(522, 2.0)], 522: [(488, 1.0)], 488: [(109, 30.0)], 109: [(799, 10.0)], 799: [(550, 86400.0)], 98: [], 796: [(688, 0.0), (982, 1.0)]}\nsource = 796\ntarget = 98\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86451.0, "source_answer": 86451.0}
{"source_row": 1077, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {197: [(607, 10.0)], 565: [(749, 0.0)], 607: [(662, 5.0)], 662: [(633, 5.0), (944, 5.0)], 633: [(528, 15.0)], 944: [(266, 5.0)], 266: [(528, 15.0)], 209: [(101, 10.0)], 528: [(209, 5.0)], 101: [(565, 5.0)], 749: [], 2: [(197, 5.0)]}\nsource = 2\ntarget = 749\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 1078, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {685: [(903, 10.0)], 541: [(791, 0.0)], 401: [(617, 2.0)], 617: [(524, 3.0)], 524: [(243, 5.0)], 243: [(297, 10.0)], 297: [(903, 10.0)], 903: [(813, 15.0)], 813: [(541, 2.0)], 409: [(791, 0.0)], 791: [], 483: [(685, 10.0), (401, 3.0), (409, 5.0)]}\nsource = 483\ntarget = 791\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1079, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {885: [(524, 3.0)], 482: [(524, 3.0)], 629: [(422, 15.0)], 422: [(805, 2.0)], 805: [(524, 3.0)], 524: [(128, 0.0)], 128: [], 259: [(885, 10.0), (482, 5.0), (629, 2.0)]}\nsource = 259\ntarget = 128\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 1080, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {875: [(651, 30.0)], 424: [(394, 0.0)], 161: [(922, 3.0)], 922: [(121, 5.0)], 121: [(651, 30.0)], 651: [(406, 2.0)], 406: [(512, 10.0), (27, 15.0), (234, 5.0)], 234: [(424, 5.0)], 512: [(424, 5.0)], 27: [(424, 5.0)], 394: [], 46: [(875, 10.0), (161, 5.0)]}\nsource = 46\ntarget = 394\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0}
{"source_row": 1081, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {933: [(973, 7.0)], 641: [(866, 0.0)], 824: [(200, 2.0)], 137: [(200, 2.0)], 200: [(267, 240.0)], 267: [(640, 15.0)], 640: [(972, 2.0)], 972: [(973, 7.0)], 973: [(641, 2.0)], 225: [(641, 2.0)], 866: [], 102: [(933, 10.0), (824, 5.0), (137, 3.0), (225, 5.0)]}\nsource = 102\ntarget = 866\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 273.0, "source_answer": 273.0}
{"source_row": 1082, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {635: [(417, 6.0)], 91: [(577, 2.0)], 577: [(761, 5.0)], 761: [(417, 6.0)], 417: [(675, 0.0)], 675: [], 318: [(635, 15.0), (91, 2.0)]}\nsource = 318\ntarget = 675\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 1083, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {322: [(243, 1.0), (547, 1.0)], 631: [(747, 0.0)], 747: [(101, 0.0)], 243: [(253, 1.0)], 547: [(253, 1.0)], 670: [(253, 1.0)], 253: [(792, 2.0)], 792: [(481, 1.0)], 481: [(667, 2.0)], 667: [(664, 120.0)], 664: [(631, 1.0)], 101: [], 359: [(322, 5.0), (670, 1.0)]}\nsource = 359\ntarget = 101\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 133.0, "source_answer": 133.0}
{"source_row": 1084, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {10: [(111, 3.0)], 389: [(116, 2.0)], 116: [(955, 0.0)], 71: [(90, 5.0)], 90: [(111, 3.0)], 111: [(100, 3.0)], 100: [(424, 3.0)], 424: [(360, 2.0)], 360: [(306, 5.0)], 306: [(675, 5.0)], 675: [(389, 5.0)], 955: [], 183: [(10, 10.0), (71, 5.0)]}\nsource = 183\ntarget = 955\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 38.0, "source_answer": 38.0}
{"source_row": 1085, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {300: [(56, 2.0)], 108: [(56, 2.0)], 56: [(302, 3.0)], 302: [(690, 20160.0)], 690: [(995, 0.0)], 995: [], 347: [(300, 0.5), (108, 1.0)]}\nsource = 347\ntarget = 995\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20166.0, "source_answer": 20166.0}
{"source_row": 1086, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {88: [(489, 2.0)], 246: [(694, 1.0)], 694: [(239, 0.0)], 489: [(307, 3.0)], 245: [(307, 3.0)], 307: [(115, 60.0)], 115: [(981, 5.0)], 981: [(220, 5.0)], 220: [(464, 15.0)], 464: [(597, 10.0)], 597: [(246, 18.0)], 239: [], 126: [(88, 5.0), (245, 3.0)]}\nsource = 126\ntarget = 239\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 124.0, "source_answer": 124.0}
{"source_row": 1087, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {974: [(241, 15.0), (31, 15.0), (239, 10.0), (418, 30.0), (346, 10.0), (17, 20.0), (219, 20.0), (484, 15.0), (749, 15.0), (665, 10.0)], 31: [(320, 0.0)], 418: [(320, 0.0)], 665: [(320, 0.0)], 749: [(320, 0.0)], 219: [(320, 0.0)], 346: [(320, 0.0)], 484: [(320, 0.0)], 241: [(320, 0.0)], 17: [(320, 0.0)], 239: [(320, 0.0)], 320: [], 880: [(974, 5.0)]}\nsource = 880\ntarget = 320\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1088, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {783: [(953, 10.0), (139, 20.0), (362, 20.0), (941, 10.0), (352, 20.0), (473, 20.0), (342, 20.0), (423, 10.0), (123, 20.0)], 139: [(721, 0.0)], 941: [(721, 0.0)], 910: [(953, 10.0), (139, 20.0), (362, 20.0), (941, 10.0), (352, 20.0), (473, 20.0), (342, 20.0), (423, 10.0), (123, 20.0)], 123: [(721, 0.0)], 342: [(721, 0.0)], 352: [(721, 0.0)], 423: [(721, 0.0)], 953: [(721, 0.0)], 473: [(721, 0.0)], 362: [(721, 0.0)], 721: [], 200: [(783, 10.0), (910, 5.0)]}\nsource = 200\ntarget = 721\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1089, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {295: [(473, 2.0)], 862: [(865, 0.0)], 865: [(745, 0.0)], 543: [(563, 3.0)], 870: [(698, 10.0)], 579: [(886, 2.0)], 698: [(480, 5.0)], 563: [(473, 2.0)], 473: [(862, 1.0)], 886: [(862, 1.0)], 480: [(862, 1.0)], 745: [], 671: [(295, 5.0), (543, 2.0), (870, 8.0), (579, 5.0)]}\nsource = 671\ntarget = 745\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24.0, "source_answer": 24.0}
{"source_row": 1090, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {36: [(583, 0.02)], 888: [(583, 0.02)], 583: [(176, 0.0)], 22: [(583, 0.02)], 845: [(583, 0.02)], 311: [(583, 0.02)], 967: [(583, 0.02)], 13: [(583, 0.02)], 566: [(583, 0.02)], 808: [(583, 0.02)], 480: [(583, 0.02)], 176: [], 559: [(36, 0.02), (22, 0.02), (845, 0.02), (311, 0.02), (967, 0.02), (13, 0.02), (566, 0.02), (808, 0.02), (480, 0.02), (888, 0.02)]}\nsource = 559\ntarget = 176\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.04, "source_answer": 0.04}
{"source_row": 1091, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {333: [(987, 45.0)], 928: [(987, 45.0)], 987: [(891, 60.0)], 891: [(431, 0.0)], 431: [], 906: [(333, 30.0), (928, 20.0)]}\nsource = 906\ntarget = 431\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 1092, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {406: [(455, 0.0)], 694: [(147, 2.0)], 293: [(578, 2.0), (694, 1.0)], 578: [(147, 2.0)], 147: [(953, 1.0), (155, 2.0)], 155: [(748, 2.0)], 953: [(748, 2.0)], 748: [(406, 5.0)], 551: [(293, 1.0)], 237: [(293, 1.0)], 69: [(293, 1.0)], 132: [(293, 1.0)], 455: [], 615: [(551, 1.0), (237, 1.0), (69, 1.0), (132, 1.0)]}\nsource = 615\ntarget = 455\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1093, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {528: [(50, 2.0)], 647: [(984, 10.0)], 984: [(50, 2.0)], 50: [(711, 240.0)], 711: [(646, 0.0)], 803: [(180, 5.0)], 861: [(353, 5.0)], 353: [(541, 10.0)], 541: [(54, 60.0)], 180: [(541, 10.0)], 54: [(984, 10.0)], 749: [(980, 7.0)], 980: [(647, 10.0)], 646: [], 26: [(528, 30.0), (803, 10.0), (861, 2.0), (749, 5.0)]}\nsource = 26\ntarget = 646\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 337.0, "source_answer": 337.0}
{"source_row": 1094, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {797: [(938, 2.0)], 938: [(262, 1.0)], 262: [(363, 2.0)], 363: [(954, 0.0)], 439: [(954, 0.0)], 954: [], 334: [(797, 5.0), (439, 3.0)]}\nsource = 334\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 1095, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {50: [(668, 2.0), (328, 2.0), (189, 2.0), (413, 2.0), (1, 2.0), (242, 2.0), (272, 2.0), (783, 2.0)], 668: [(939, 0.0)], 783: [(939, 0.0)], 189: [(939, 0.0)], 1: [(939, 0.0)], 431: [(668, 2.0), (328, 2.0), (413, 2.0), (189, 2.0), (242, 2.0), (1, 2.0), (272, 2.0), (783, 2.0)], 469: [(783, 2.0), (328, 2.0), (189, 2.0), (668, 2.0), (413, 2.0), (242, 2.0), (1, 2.0), (272, 2.0)], 703: [(783, 2.0), (668, 2.0), (328, 2.0), (413, 2.0), (189, 2.0), (1, 2.0), (272, 2.0), (242, 2.0)], 610: [(783, 2.0), (328, 2.0), (189, 2.0), (668, 2.0), (413, 2.0), (242, 2.0), (1, 2.0), (272, 2.0)], 328: [(939, 0.0)], 272: [(939, 0.0)], 413: [(939, 0.0)], 242: [(939, 0.0)], 939: [], 66: [(50, 5.0), (431, 2.0), (469, 3.0), (703, 4.0), (610, 2.0)]}\nsource = 66\ntarget = 939\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1096, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {205: [(731, 30.0)], 871: [(927, 10.0)], 927: [(447, 30.0)], 447: [(731, 30.0)], 731: [(184, 0.0)], 184: [], 497: [(205, 30.0), (871, 30.0)]}\nsource = 497\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0}
{"source_row": 1097, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {835: [(65, 15.0), (62, 60.0)], 62: [(87, 0.0)], 65: [(167, 60.0)], 167: [(971, 60.0)], 971: [(87, 0.0)], 87: [], 591: [(835, 10080.0)]}\nsource = 591\ntarget = 87\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10215.0, "source_answer": 10215.0}
{"source_row": 1098, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(290, 45.0)], 290: [(33, 4320.0)], 33: [(802, 30.0)], 802: [(415, 129600.0), (211, 4320.0)], 211: [(742, 0.0)], 415: [(742, 0.0)], 742: [], 513: [(589, 45.0)]}\nsource = 513\ntarget = 742\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 134040.0, "source_answer": 134040.0}
{"source_row": 1099, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {985: [(165, 0.08)], 165: [(309, 0.08)], 309: [(351, 0.0)], 14: [(789, 3.0)], 789: [(309, 0.08)], 351: [], 220: [(985, 3.0), (14, 5.0)]}\nsource = 220\ntarget = 351\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.08, "source_answer": 8.08}
{"source_row": 1100, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {981: [(154, 25.0)], 492: [(154, 25.0)], 154: [(330, 5.0)], 330: [(804, 15.0)], 804: [(194, 25.0)], 194: [(872, 0.0)], 872: [], 519: [(981, 5.0), (492, 15.0)]}\nsource = 519\ntarget = 872\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 1101, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {158: [(357, 129600.0)], 991: [(576, 60.0)], 576: [(937, 0.0)], 835: [(357, 129600.0)], 357: [(576, 60.0)], 937: [], 154: [(158, 1440.0), (991, 43200.0), (835, 10.0)]}\nsource = 154\ntarget = 937\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 131100.0, "source_answer": 131100.0}
{"source_row": 1102, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {5: [(397, 60.0)], 764: [(733, 259200.0)], 733: [(947, 129600.0)], 947: [(397, 60.0)], 397: [(349, 10080.0)], 349: [(101, 0.0)], 101: [], 853: [(5, 43200.0), (764, 60.0)]}\nsource = 853\ntarget = 101\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 399000.0, "source_answer": 399000.0}
{"source_row": 1103, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {519: [(217, 15.0)], 217: [(685, 5.0), (521, 15.0)], 521: [(592, 5.0)], 685: [(592, 5.0)], 592: [(959, 0.0)], 959: [], 684: [(519, 15.0)]}\nsource = 684\ntarget = 959\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1104, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {549: [(752, 5.0)], 752: [(932, 10080.0), (521, 1.0)], 521: [(347, 30.0)], 932: [(347, 30.0)], 347: [(191, 0.0)], 191: [], 634: [(549, 10.0)]}\nsource = 634\ntarget = 191\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10125.0, "source_answer": 10125.0}
{"source_row": 1105, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {739: [(304, 5.0)], 174: [(656, 5.0)], 656: [(51, 45.0)], 51: [(80, 5.0)], 80: [(327, 0.0)], 304: [(51, 45.0)], 327: [], 57: [(739, 30.0), (174, 60.0)]}\nsource = 57\ntarget = 327\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 115.0, "source_answer": 115.0}
{"source_row": 1106, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {508: [(665, 30.0)], 665: [(110, 5.0), (193, 240.0)], 193: [(289, 7200.0)], 110: [(289, 7200.0)], 289: [(327, 60.0)], 327: [(221, 0.0)], 221: [], 554: [(508, 1.0)]}\nsource = 554\ntarget = 221\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7531.0, "source_answer": 7531.0}
{"source_row": 1107, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {870: [(876, 15.0)], 876: [(814, 10.0)], 814: [(254, 5.0)], 254: [(376, 10.0)], 376: [(131, 15.0), (566, 15.0)], 131: [(175, 0.0)], 566: [(175, 0.0)], 175: [], 197: [(870, 5.0)]}\nsource = 197\ntarget = 175\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1108, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {96: [(775, 0.5)], 387: [(775, 0.5)], 775: [(233, 0.0)], 268: [(775, 0.5)], 867: [(775, 0.5)], 233: [], 589: [(96, 180.0), (387, 300.0), (268, 180.0), (867, 180.0)]}\nsource = 589\ntarget = 233\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.5, "source_answer": 300.5}
{"source_row": 1109, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {597: [(510, 10.0)], 510: [(239, 10.0)], 239: [(659, 0.5), (677, 0.17), (943, 0.5)], 677: [(422, 0.0)], 943: [(422, 0.0)], 659: [(422, 0.0)], 422: [], 415: [(597, 10.0)]}\nsource = 415\ntarget = 422\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.5, "source_answer": 30.5}
{"source_row": 1110, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {168: [(367, 10080.0)], 565: [(287, 10080.0)], 287: [(367, 10080.0)], 367: [(382, 10080.0)], 382: [(878, 86400.0)], 878: [(602, 0.0)], 602: [], 929: [(168, 172800.0), (565, 1.0)]}\nsource = 929\ntarget = 602\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 279360.0, "source_answer": 279360.0}
{"source_row": 1111, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(999, 0.5), (25, 1.0), (533, 3.0), (655, 0.17)], 999: [(586, 0.33)], 533: [(586, 0.33)], 25: [(586, 0.33)], 655: [(586, 0.33)], 586: [(469, 20.0)], 469: [(758, 0.0)], 758: [], 455: [(505, 1.0)]}\nsource = 455\ntarget = 758\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 24.33, "source_answer": 24.33}
{"source_row": 1112, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {518: [(936, 30.0)], 61: [(936, 30.0)], 936: [(762, 20.0)], 762: [(640, 10.0)], 640: [(688, 0.0)], 688: [], 329: [(518, 2.0), (61, 10.0)]}\nsource = 329\ntarget = 688\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 1113, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {992: [(201, 15.0), (857, 20.0)], 201: [(99, 15.0)], 857: [(99, 15.0)], 99: [(900, 20.0)], 900: [(260, 0.0)], 260: [], 382: [(992, 20.0)]}\nsource = 382\ntarget = 260\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 1114, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {605: [(470, 45.0)], 470: [(189, 60.0)], 189: [(410, 15.0)], 158: [(646, 30.0)], 646: [(189, 60.0)], 410: [(181, 0.0)], 181: [], 729: [(605, 10.0), (158, 5.0)]}\nsource = 729\ntarget = 181\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130.0, "source_answer": 130.0}
{"source_row": 1115, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {875: [(716, 2880.0)], 716: [(122, 525600.0), (459, 1576800.0)], 459: [(517, 525600.0)], 122: [(517, 525600.0)], 517: [(346, 129600.0)], 346: [(637, 0.0)], 637: [], 436: [(875, 7200.0)]}\nsource = 436\ntarget = 637\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2242080.0, "source_answer": 2242080.0}
{"source_row": 1116, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {710: [(394, 30.0), (87, 30.0)], 394: [(451, 10.0)], 451: [(533, 30.0)], 533: [(581, 0.0)], 87: [(451, 10.0)], 581: [], 684: [(710, 30.0)]}\nsource = 684\ntarget = 581\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0}
{"source_row": 1117, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {634: [(53, 1.0), (555, 1.0)], 53: [(898, 10.0)], 555: [(898, 10.0)], 898: [(246, 0.5), (819, 20.0), (38, 1.0)], 819: [(469, 0.0)], 246: [(469, 0.0)], 38: [(469, 0.0)], 469: [], 821: [(634, 10.0)]}\nsource = 821\ntarget = 469\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 1118, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {831: [(691, 120.0)], 691: [(664, 30.0), (596, 180.0)], 596: [(726, 60.0)], 664: [(726, 60.0)], 726: [(68, 30.0)], 68: [(378, 120.0)], 378: [(407, 0.0)], 407: [], 81: [(831, 30.0)]}\nsource = 81\ntarget = 407\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 540.0, "source_answer": 540.0}
{"source_row": 1119, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {28: [(436, 15.0)], 628: [(136, 1.0)], 136: [(940, 5.0)], 940: [(207, 60.0)], 436: [(136, 1.0)], 207: [(422, 2.0)], 422: [(832, 0.0)], 832: [], 629: [(28, 5.0), (628, 60.0)]}\nsource = 629\ntarget = 832\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 128.0, "source_answer": 128.0}
{"source_row": 1120, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {753: [(135, 15.0)], 135: [(93, 15.0)], 93: [(919, 15.0)], 919: [(122, 30.0)], 122: [(952, 60.0), (140, 30.0)], 952: [(399, 0.0)], 140: [(399, 0.0)], 399: [], 395: [(753, 15.0)]}\nsource = 395\ntarget = 399\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 1121, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {477: [(970, 10.0)], 263: [(970, 10.0)], 970: [(507, 5.0)], 507: [(602, 5.0)], 602: [(668, 0.0)], 668: [], 687: [(477, 5.0), (263, 5.0)]}\nsource = 687\ntarget = 668\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1122, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {379: [(755, 5.0), (748, 3.0)], 755: [(578, 1.0), (746, 2.0)], 748: [(522, 1.0)], 578: [(522, 1.0)], 746: [(522, 1.0)], 522: [(855, 0.0)], 855: [], 391: [(379, 1.0)]}\nsource = 391\ntarget = 855\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1123, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {407: [(980, 0.08)], 980: [(142, 0.08)], 142: [(5, 0.08)], 5: [(880, 1.0)], 149: [(692, 0.08)], 692: [(142, 0.08)], 880: [(481, 0.0)], 481: [], 660: [(407, 0.08), (149, 0.08)]}\nsource = 660\ntarget = 481\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.32, "source_answer": 1.32}
{"source_row": 1124, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {300: [(935, 15.0)], 935: [(87, 60.0), (792, 5.0)], 792: [(133, 1.0)], 87: [(133, 1.0)], 133: [(953, 60.0)], 953: [(690, 1.0)], 690: [(451, 0.0)], 451: [], 303: [(300, 5.0)]}\nsource = 303\ntarget = 451\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 142.0, "source_answer": 142.0}
{"source_row": 1125, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {689: [(865, 0.08)], 949: [(865, 0.08)], 865: [(458, 0.17)], 458: [(203, 1.0)], 203: [(142, 10.0)], 142: [(930, 0.0)], 930: [], 157: [(689, 0.08), (949, 5.0)]}\nsource = 157\ntarget = 930\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.25, "source_answer": 16.25}
{"source_row": 1126, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {583: [(760, 4.0)], 760: [(191, 1.0)], 191: [(389, 15.0)], 389: [(228, 10.0)], 228: [(938, 0.5), (354, 5.0)], 938: [(829, 0.0)], 354: [(829, 0.0)], 829: [], 221: [(583, 2.0)]}\nsource = 221\ntarget = 829\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 1127, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {449: [(638, 15.0)], 672: [(153, 15.0)], 153: [(656, 5.0)], 656: [(898, 25.0)], 638: [(656, 5.0)], 898: [(609, 15.0)], 609: [(673, 0.0)], 673: [], 18: [(449, 15.0), (672, 15.0)]}\nsource = 18\ntarget = 673\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 75.0, "source_answer": 75.0}
{"source_row": 1128, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {491: [(298, 45.0)], 655: [(298, 45.0)], 298: [(903, 15.0)], 903: [(103, 25.0)], 103: [(646, 120.0)], 646: [(796, 0.0)], 796: [], 153: [(491, 120.0), (655, 15.0)]}\nsource = 153\ntarget = 796\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 325.0, "source_answer": 325.0}
{"source_row": 1129, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {145: [(789, 15.0)], 789: [(734, 15.0)], 734: [(171, 7.0), (357, 7.0)], 171: [(725, 15.0)], 357: [(725, 15.0)], 725: [(817, 3.0)], 817: [(783, 0.0)], 783: [], 294: [(145, 15.0)]}\nsource = 294\ntarget = 783\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0}
{"source_row": 1130, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {292: [(909, 15.0), (40, 30.0)], 909: [(573, 15.0)], 40: [(573, 15.0)], 573: [(732, 30.0)], 732: [(531, 180.0)], 531: [(216, 15.0)], 216: [(54, 0.0)], 54: [], 375: [(292, 30.0)]}\nsource = 375\ntarget = 54\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 1131, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {345: [(56, 5.0)], 365: [(980, 1.0)], 980: [(371, 1.0)], 371: [(56, 5.0)], 56: [(994, 20.0)], 994: [(505, 1.0)], 505: [(45, 0.0)], 45: [], 538: [(345, 15.0), (365, 5.0)]}\nsource = 538\ntarget = 45\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.0, "source_answer": 41.0}
{"source_row": 1132, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {710: [(572, 1440.0)], 247: [(572, 1440.0)], 572: [(517, 1440.0)], 517: [(779, 43200.0)], 779: [(767, 0.0)], 767: [], 608: [(710, 10080.0), (247, 4320.0)]}\nsource = 608\ntarget = 767\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 56160.0, "source_answer": 56160.0}
{"source_row": 1133, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {488: [(736, 1.0)], 54: [(574, 15.0)], 574: [(736, 1.0)], 736: [(712, 1.0)], 712: [(253, 0.0)], 253: [], 632: [(488, 0.5), (54, 5.0)]}\nsource = 632\ntarget = 253\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 1134, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {606: [(80, 0.17)], 565: [(18, 3.0)], 18: [(241, 0.17)], 241: [(279, 10.0)], 279: [(570, 0.17)], 80: [(241, 0.17)], 570: [(817, 0.0)], 817: [], 757: [(606, 5.0), (565, 3.0)]}\nsource = 757\ntarget = 817\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.34, "source_answer": 16.34}
{"source_row": 1135, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {201: [(828, 60.0)], 828: [(277, 60.0)], 277: [(880, 10080.0), (5, 7200.0)], 5: [(614, 60.0)], 614: [(68, 0.0)], 880: [(614, 60.0)], 68: [], 673: [(201, 15.0)]}\nsource = 673\ntarget = 68\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10275.0, "source_answer": 10275.0}
{"source_row": 1136, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {516: [(126, 2880.0)], 126: [(525, 10.0), (364, 300.0)], 364: [(156, 3.0)], 525: [(156, 3.0)], 156: [(916, 0.0)], 916: [], 438: [(516, 180.0)]}\nsource = 438\ntarget = 916\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3363.0, "source_answer": 3363.0}
{"source_row": 1137, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(756, 525600.0), (313, 43200.0)], 756: [(788, 43200.0)], 313: [(788, 43200.0)], 788: [(975, 10080.0)], 975: [(935, 0.0)], 935: [], 421: [(458, 1051200.0)]}\nsource = 421\ntarget = 935\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1630080.0, "source_answer": 1630080.0}
{"source_row": 1138, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {645: [(518, 5.0)], 152: [(518, 5.0)], 518: [(256, 5.0)], 256: [(605, 10.0)], 605: [(941, 10.0)], 941: [(954, 0.0)], 954: [], 31: [(645, 30.0), (152, 120.0)]}\nsource = 31\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 1139, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {958: [(407, 20.0), (114, 5.0)], 407: [(805, 15.0)], 114: [(805, 15.0)], 805: [(654, 5.0)], 654: [(350, 0.0)], 350: [], 185: [(958, 3.0)]}\nsource = 185\ntarget = 350\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43.0, "source_answer": 43.0}
{"source_row": 1140, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {541: [(150, 10.0)], 429: [(453, 10.0)], 453: [(171, 5.0)], 171: [(316, 0.0)], 150: [(453, 10.0)], 316: [], 283: [(541, 60.0), (429, 1440.0)]}\nsource = 283\ntarget = 316\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1455.0, "source_answer": 1455.0}
{"source_row": 1141, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {152: [(320, 0.17), (432, 0.17), (988, 0.17)], 988: [(876, 0.33)], 432: [(240, 0.0)], 320: [(240, 0.0)], 876: [(240, 0.0)], 240: [], 456: [(152, 1.0)]}\nsource = 456\ntarget = 240\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.5, "source_answer": 1.5}
{"source_row": 1142, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {622: [(614, 13.0)], 519: [(934, 5.0)], 934: [(33, 2.0)], 33: [(646, 0.0)], 614: [(646, 0.0)], 646: [], 436: [(622, 5.0), (519, 5.0)]}\nsource = 436\ntarget = 646\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 1143, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {958: [(298, 1440.0)], 298: [(520, 5.0)], 520: [(517, 360.0), (279, 360.0)], 517: [(340, 0.0)], 279: [(340, 0.0)], 340: [], 973: [(958, 129600.0)]}\nsource = 973\ntarget = 340\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 131405.0, "source_answer": 131405.0}
{"source_row": 1144, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {432: [(166, 180.0)], 166: [(252, 10080.0)], 252: [(663, 10080.0), (814, 10080.0)], 663: [(755, 0.0)], 814: [(755, 0.0)], 755: [], 54: [(432, 10080.0)]}\nsource = 54\ntarget = 755\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30420.0, "source_answer": 30420.0}
{"source_row": 1145, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {780: [(409, 60.0), (129, 60.0)], 409: [(707, 120.0)], 707: [(177, 10.0)], 177: [(984, 60.0)], 129: [(707, 120.0)], 984: [(231, 0.0)], 231: [], 915: [(780, 30.0)]}\nsource = 915\ntarget = 231\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 280.0, "source_answer": 280.0}
{"source_row": 1146, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {443: [(269, 120.0), (377, 1440.0)], 269: [(997, 0.0)], 377: [(71, 1440.0)], 71: [(836, 60.0)], 836: [(117, 60.0)], 117: [(997, 0.0)], 997: [], 879: [(443, 180.0)]}\nsource = 879\ntarget = 997\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3180.0, "source_answer": 3180.0}
{"source_row": 1147, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {682: [(326, 300.0), (761, 300.0)], 326: [(332, 300.0)], 761: [(332, 300.0)], 332: [(293, 180.0)], 293: [(526, 60.0)], 526: [(656, 0.0)], 656: [], 739: [(682, 300.0)]}\nsource = 739\ntarget = 656\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1140.0, "source_answer": 1140.0}
{"source_row": 1148, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {527: [(580, 10080.0)], 580: [(358, 7200.0), (18, 10080.0)], 18: [(425, 432000.0)], 358: [(425, 432000.0)], 425: [(807, 0.0)], 807: [], 705: [(527, 129600.0)]}\nsource = 705\ntarget = 807\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 581760.0, "source_answer": 581760.0}
{"source_row": 1149, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {344: [(179, 1.0), (662, 5.0)], 179: [(779, 0.03)], 662: [(382, 0.05)], 779: [(279, 1.0)], 382: [(279, 1.0)], 279: [(943, 0.08)], 943: [(944, 0.0)], 944: [], 557: [(344, 0.5)]}\nsource = 557\ntarget = 944\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.63, "source_answer": 6.63}
{"source_row": 1150, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {795: [(850, 0.42)], 850: [(858, 0.58), (532, 0.58)], 532: [(403, 10080.0)], 858: [(403, 10080.0)], 403: [(763, 0.0)], 763: [], 933: [(795, 0.42)]}\nsource = 933\ntarget = 763\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10081.42, "source_answer": 10081.42}
{"source_row": 1151, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {434: [(335, 60.0)], 335: [(570, 10.0), (861, 30.0)], 861: [(834, 10.0)], 570: [(834, 10.0)], 834: [(72, 30.0)], 72: [(421, 30.0)], 421: [(172, 0.0)], 172: [], 221: [(434, 1.0)]}\nsource = 221\ntarget = 172\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 161.0, "source_answer": 161.0}
{"source_row": 1152, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {818: [(744, 0.5)], 583: [(744, 0.5)], 744: [(506, 0.5)], 506: [(974, 0.5)], 974: [(713, 5.0)], 713: [(765, 1.0)], 765: [(908, 0.0)], 908: [], 349: [(818, 1.0), (583, 1.0)]}\nsource = 349\ntarget = 908\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.5, "source_answer": 8.5}
{"source_row": 1153, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {110: [(245, 0.42)], 197: [(245, 0.42)], 245: [(637, 0.58)], 637: [(526, 0.0)], 653: [(245, 0.42)], 526: [], 917: [(110, 0.42), (197, 0.42), (653, 0.42)]}\nsource = 917\ntarget = 526\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.42, "source_answer": 1.42}
{"source_row": 1154, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {128: [(256, 0.25), (828, 0.17)], 256: [(251, 0.5)], 828: [(251, 0.5)], 251: [(830, 0.75)], 830: [(797, 0.0)], 797: [], 200: [(128, 0.17)]}\nsource = 200\ntarget = 797\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.67, "source_answer": 1.67}
{"source_row": 1155, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {334: [(886, 1.0)], 218: [(886, 1.0)], 886: [(465, 0.25), (777, 3.0)], 465: [(666, 0.0)], 777: [(666, 0.0)], 666: [], 702: [(334, 3.0), (218, 5.0)]}\nsource = 702\ntarget = 666\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1156, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {397: [(682, 0.17), (588, 0.17), (630, 0.17)], 682: [(645, 0.17)], 630: [(278, 0.17)], 588: [(269, 0.33)], 645: [(998, 0.0)], 278: [(998, 0.0)], 269: [(998, 0.0)], 998: [], 698: [(397, 0.08)]}\nsource = 698\ntarget = 998\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.58, "source_answer": 0.58}
{"source_row": 1157, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {851: [(206, 43200.0), (665, 7200.0)], 206: [(163, 5.0)], 163: [(655, 3.0)], 655: [(190, 0.0)], 665: [(163, 5.0)], 190: [], 94: [(851, 20160.0)]}\nsource = 94\ntarget = 190\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63368.0, "source_answer": 63368.0}
{"source_row": 1158, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {332: [(394, 10.0)], 394: [(234, 5.0)], 234: [(902, 15.0), (149, 5.0)], 902: [(169, 15.0)], 149: [(169, 15.0)], 169: [(882, 5.0)], 882: [(756, 0.0)], 756: [], 980: [(332, 5.0)]}\nsource = 980\ntarget = 756\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1159, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {698: [(217, 0.17)], 217: [(157, 1.0)], 157: [(0, 0.33), (224, 0.5)], 0: [(110, 1.0)], 224: [(110, 1.0)], 110: [(522, 0.0)], 522: [], 487: [(698, 0.5)]}\nsource = 487\ntarget = 522\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.17, "source_answer": 3.17}
{"source_row": 1160, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {26: [(723, 0.5)], 852: [(723, 0.5)], 723: [(74, 0.5)], 74: [(681, 10.0)], 681: [(404, 0.0)], 404: [], 456: [(26, 0.5), (852, 0.33)]}\nsource = 456\ntarget = 404\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.5, "source_answer": 11.5}
{"source_row": 1161, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {807: [(405, 1.0)], 405: [(991, 5.0)], 991: [(545, 1.0), (875, 1.0)], 545: [(776, 1.0)], 875: [(776, 1.0)], 776: [(240, 0.0)], 240: [], 551: [(807, 60.0)]}\nsource = 551\ntarget = 240\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 68.0, "source_answer": 68.0}
{"source_row": 1162, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {565: [(137, 1.0), (267, 1.0)], 137: [(509, 0.5)], 267: [(509, 0.5)], 509: [(560, 0.5)], 560: [(283, 0.0)], 283: [], 979: [(565, 2.0)]}\nsource = 979\ntarget = 283\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1163, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {862: [(410, 0.5)], 410: [(489, 0.25), (503, 0.25)], 503: [(99, 0.17)], 489: [(468, 0.17)], 99: [(276, 0.0)], 468: [(276, 0.0)], 276: [], 806: [(862, 0.75)]}\nsource = 806\ntarget = 276\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.67, "source_answer": 1.67}
{"source_row": 1164, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {734: [(812, 5.0), (592, 1.0)], 812: [(33, 2.0)], 592: [(33, 2.0)], 33: [(760, 1.0)], 760: [(619, 1.0)], 619: [(854, 0.0)], 854: [], 467: [(734, 2880.0)]}\nsource = 467\ntarget = 854\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2889.0, "source_answer": 2889.0}
{"source_row": 1165, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {57: [(783, 0.5)], 856: [(783, 0.5)], 783: [(874, 0.5)], 874: [(210, 0.0)], 756: [(154, 2.0)], 154: [(874, 0.5)], 210: [], 48: [(57, 0.5), (856, 0.5), (756, 0.5)]}\nsource = 48\ntarget = 210\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 1166, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {378: [(585, 0.17)], 585: [(332, 0.33)], 332: [(885, 0.17), (925, 0.17)], 885: [(948, 0.0)], 925: [(948, 0.0)], 948: [], 922: [(378, 0.25)]}\nsource = 922\ntarget = 948\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.92, "source_answer": 0.92}
{"source_row": 1167, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {398: [(880, 5.0), (314, 2.0)], 880: [(202, 30.0)], 314: [(202, 30.0), (487, 5.0)], 202: [(941, 0.0)], 487: [(941, 0.0)], 941: [], 862: [(398, 20.0)]}\nsource = 862\ntarget = 941\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1168, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {975: [(111, 0.5)], 111: [(18, 1.0)], 18: [(599, 2.0)], 599: [(58, 5.0), (847, 30.0)], 847: [(419, 2.0)], 58: [(419, 2.0)], 419: [(913, 0.0)], 913: [], 898: [(975, 0.5)]}\nsource = 898\ntarget = 913\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36.0, "source_answer": 36.0}
{"source_row": 1169, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {299: [(721, 30.0)], 721: [(254, 60.0)], 254: [(90, 60.0)], 152: [(254, 60.0)], 90: [(540, 0.0)], 540: [], 694: [(299, 60.0), (152, 120.0)]}\nsource = 694\ntarget = 540\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 1170, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {344: [(796, 20.0)], 998: [(252, 0.0)], 796: [(311, 60.0), (998, 30.0)], 311: [(587, 480.0)], 587: [(252, 0.0)], 252: [], 632: [(344, 3.0)]}\nsource = 632\ntarget = 252\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 563.0, "source_answer": 563.0}
{"source_row": 1171, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {641: [(293, 0.17)], 627: [(454, 15.0)], 454: [(988, 60.0)], 988: [(786, 0.0)], 518: [(454, 15.0)], 293: [(650, 60.0)], 650: [(454, 15.0)], 786: [], 354: [(641, 0.58), (627, 0.58), (518, 0.58)]}\nsource = 354\ntarget = 786\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.75, "source_answer": 135.75}
{"source_row": 1172, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {129: [(466, 60.0)], 103: [(780, 0.5)], 780: [(60, 0.08)], 60: [(466, 60.0)], 466: [(471, 2.0)], 471: [(514, 5.0)], 514: [(672, 0.0)], 672: [], 278: [(129, 0.08), (103, 2.0)]}\nsource = 278\ntarget = 672\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 69.58, "source_answer": 69.58}
{"source_row": 1173, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {92: [(64, 7200.0)], 64: [(50, 525600.0)], 50: [(630, 120.0), (197, 525600.0)], 630: [(259, 30.0)], 197: [(259, 30.0)], 259: [(385, 0.0)], 385: [], 156: [(92, 30.0)]}\nsource = 156\ntarget = 385\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1058460.0, "source_answer": 1058460.0}
{"source_row": 1174, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {734: [(737, 30.0)], 677: [(980, 5.0)], 980: [(695, 2.0)], 695: [(83, 28800.0)], 83: [(530, 0.0)], 737: [(784, 2.0)], 784: [(695, 2.0)], 530: [], 122: [(734, 300.0), (677, 60.0)]}\nsource = 122\ntarget = 530\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 29134.0, "source_answer": 29134.0}
{"source_row": 1175, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {130: [(178, 43200.0), (434, 43200.0)], 178: [(401, 1440.0)], 434: [(401, 1440.0)], 401: [(109, 30.0)], 109: [(665, 0.0)], 665: [], 347: [(130, 10.0)]}\nsource = 347\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 44680.0, "source_answer": 44680.0}
{"source_row": 1176, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {598: [(775, 30240.0)], 763: [(9, 86400.0)], 9: [(412, 4320.0)], 412: [(775, 30240.0)], 775: [(98, 0.0)], 98: [], 899: [(598, 43200.0), (763, 43200.0)]}\nsource = 899\ntarget = 98\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 164160.0, "source_answer": 164160.0}
{"source_row": 1177, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {272: [(583, 43200.0)], 583: [(15, 60.0), (753, 60.0)], 753: [(100, 60.0)], 15: [(100, 60.0)], 100: [(210, 15.0)], 210: [(470, 43200.0)], 470: [(723, 0.0)], 723: [], 276: [(272, 60.0)]}\nsource = 276\ntarget = 723\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86595.0, "source_answer": 86595.0}
{"source_row": 1178, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {660: [(813, 1.0)], 315: [(813, 1.0)], 813: [(377, 20.0)], 377: [(880, 2.0)], 880: [(450, 0.0)], 450: [], 216: [(660, 5.0), (315, 5.0)]}\nsource = 216\ntarget = 450\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0}
{"source_row": 1179, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {961: [(549, 30.0)], 549: [(813, 30.0), (553, 60.0)], 553: [(317, 10.0)], 813: [(317, 10.0)], 317: [(882, 30.0)], 882: [(889, 0.0)], 889: [], 243: [(961, 10.0)]}\nsource = 243\ntarget = 889\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 140.0, "source_answer": 140.0}
{"source_row": 1180, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {865: [(33, 120.0), (126, 60.0)], 33: [(912, 60.0)], 126: [(912, 60.0)], 912: [(805, 30.0)], 805: [(666, 3.0)], 666: [(853, 3.0)], 853: [(620, 0.0)], 620: [], 336: [(865, 10.0)]}\nsource = 336\ntarget = 620\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 226.0, "source_answer": 226.0}
{"source_row": 1181, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {957: [(822, 129600.0), (119, 60.0)], 822: [(795, 4320.0)], 119: [(410, 60.0)], 795: [(488, 180.0)], 410: [(893, 5.0)], 488: [(410, 60.0)], 893: [(443, 0.0)], 443: [], 489: [(957, 525600.0)]}\nsource = 489\ntarget = 443\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 659765.0, "source_answer": 659765.0}
{"source_row": 1182, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {800: [(899, 15.0)], 899: [(608, 20.0)], 608: [(844, 5.0), (961, 5.0)], 844: [(612, 60.0)], 961: [(472, 60.0)], 612: [(472, 60.0)], 472: [(18, 0.0)], 18: [], 95: [(800, 10.0)]}\nsource = 95\ntarget = 18\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 170.0, "source_answer": 170.0}
{"source_row": 1183, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {651: [(528, 0.0)], 479: [(90, 180.0)], 90: [(467, 120.0)], 467: [(528, 0.0)], 772: [(528, 0.0)], 528: [], 367: [(651, 300.0), (479, 300.0), (772, 180.0)]}\nsource = 367\ntarget = 528\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 600.0, "source_answer": 600.0}
{"source_row": 1184, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {64: [(45, 20.0)], 308: [(935, 60.0)], 935: [(886, 0.0)], 45: [(483, 15.0)], 483: [(572, 50.0), (308, 15.0)], 572: [(935, 60.0)], 886: [], 388: [(64, 5.0)]}\nsource = 388\ntarget = 886\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 1185, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {407: [(839, 1.0)], 490: [(141, 1.0)], 141: [(25, 0.42)], 25: [(455, 1.0)], 839: [(585, 2.0)], 585: [(184, 0.0)], 455: [(839, 1.0)], 184: [], 180: [(407, 1.0), (490, 0.33)]}\nsource = 180\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.75, "source_answer": 5.75}
{"source_row": 1186, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {195: [(407, 86400.0), (3, 86400.0), (7, 86400.0), (389, 86400.0)], 389: [(831, 0.0)], 7: [(831, 0.0)], 3: [(831, 0.0)], 407: [(831, 0.0)], 831: [], 525: [(195, 60.0)]}\nsource = 525\ntarget = 831\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86460.0, "source_answer": 86460.0}
{"source_row": 1187, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {803: [(938, 0.25)], 938: [(166, 0.17), (612, 0.17)], 612: [(519, 0.42)], 166: [(194, 0.42)], 519: [(921, 0.17)], 194: [(921, 0.17)], 921: [(655, 0.0)], 655: [], 196: [(803, 0.17)]}\nsource = 196\ntarget = 655\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.18, "source_answer": 1.18}
{"source_row": 1188, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {2: [(582, 10.0)], 120: [(582, 10.0)], 582: [(319, 1.0)], 319: [(998, 1.0)], 998: [(177, 0.0)], 177: [], 682: [(2, 1.0), (120, 0.08)]}\nsource = 682\ntarget = 177\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 1189, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {276: [(181, 0.33)], 181: [(34, 2.0)], 34: [(931, 10.0), (136, 10.0)], 931: [(498, 10.0)], 136: [(498, 10.0)], 498: [(7, 0.0)], 7: [], 651: [(276, 0.02)]}\nsource = 651\ntarget = 7\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.35, "source_answer": 22.35}
{"source_row": 1190, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {212: [(203, 5.0), (192, 2880.0)], 203: [(954, 15.0)], 954: [(847, 2880.0)], 847: [(525, 0.0)], 192: [(954, 15.0)], 525: [], 174: [(212, 4320.0)]}\nsource = 174\ntarget = 525\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10095.0, "source_answer": 10095.0}
{"source_row": 1191, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {10: [(776, 60.0)], 364: [(776, 60.0)], 776: [(778, 120.0)], 778: [(441, 5.0)], 441: [(877, 20.0)], 877: [(442, 0.0)], 442: [], 565: [(10, 120.0), (364, 10.0)]}\nsource = 565\ntarget = 442\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 325.0, "source_answer": 325.0}
{"source_row": 1192, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {679: [(367, 60.0)], 367: [(662, 86400.0)], 662: [(627, 43200.0), (567, 86400.0)], 627: [(747, 0.0)], 567: [(747, 0.0)], 747: [], 331: [(679, 60.0)]}\nsource = 331\ntarget = 747\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 172920.0, "source_answer": 172920.0}
{"source_row": 1193, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {935: [(290, 15.0)], 498: [(410, 1.0)], 410: [(879, 129600.0)], 879: [(563, 0.0)], 290: [(410, 1.0)], 563: [], 24: [(935, 15.0), (498, 15.0)]}\nsource = 24\ntarget = 563\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129631.0, "source_answer": 129631.0}
{"source_row": 1194, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {521: [(115, 360.0)], 727: [(115, 360.0)], 115: [(767, 60.0)], 767: [(67, 300.0)], 67: [(599, 0.0)], 599: [], 336: [(521, 60.0), (727, 60.0)]}\nsource = 336\ntarget = 599\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 780.0, "source_answer": 780.0}
{"source_row": 1195, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {536: [(766, 1440.0), (850, 120.0), (289, 60.0)], 766: [(29, 10.0)], 289: [(29, 10.0)], 850: [(29, 10.0)], 29: [(194, 10.0)], 194: [(977, 0.5)], 977: [(83, 0.0)], 83: [], 450: [(536, 1440.0)]}\nsource = 450\ntarget = 83\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2900.5, "source_answer": 2900.5}
{"source_row": 1196, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {958: [(687, 10.0)], 242: [(124, 20.0)], 124: [(687, 10.0)], 687: [(817, 1.0)], 817: [(293, 2.0)], 293: [(151, 0.0)], 151: [], 501: [(958, 15.0), (242, 30.0)]}\nsource = 501\ntarget = 151\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63.0, "source_answer": 63.0}
{"source_row": 1197, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {22: [(471, 10.0)], 694: [(71, 0.0)], 92: [(976, 5.0)], 976: [(71, 0.0)], 471: [(694, 10.0)], 71: [], 941: [(22, 10.0), (92, 15.0)]}\nsource = 941\ntarget = 71\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1198, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {832: [(53, 43200.0), (698, 14400.0)], 53: [(55, 43200.0)], 698: [(55, 43200.0)], 55: [(885, 43200.0)], 885: [(52, 0.0)], 52: [], 175: [(832, 5.0)]}\nsource = 175\ntarget = 52\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129605.0, "source_answer": 129605.0}
{"source_row": 1199, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {471: [(933, 15.0)], 874: [(962, 10.0)], 962: [(452, 0.0)], 933: [(276, 5.0)], 276: [(452, 0.0)], 452: [], 124: [(471, 20.0), (874, 10.0)]}\nsource = 124\ntarget = 452\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0}
{"source_row": 1200, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {547: [(627, 20.0)], 627: [(161, 5.0)], 161: [(802, 0.5), (288, 0.5)], 802: [(503, 0.0)], 288: [(503, 0.0)], 503: [], 463: [(547, 2.0)]}\nsource = 463\ntarget = 503\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.5, "source_answer": 27.5}
{"source_row": 1201, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {395: [(794, 1440.0)], 681: [(794, 1440.0)], 794: [(417, 1440.0)], 417: [(582, 1440.0)], 582: [(921, 0.0)], 921: [], 310: [(395, 1440.0), (681, 1440.0)]}\nsource = 310\ntarget = 921\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5760.0, "source_answer": 5760.0}
{"source_row": 1202, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {934: [(250, 120.0), (823, 180.0)], 740: [(836, 15.0)], 836: [(250, 120.0), (823, 180.0)], 250: [(96, 0.0)], 823: [(96, 0.0)], 96: [], 289: [(934, 2880.0), (740, 20.0)]}\nsource = 289\ntarget = 96\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3060.0, "source_answer": 3060.0}
{"source_row": 1203, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {958: [(579, 120.0), (591, 10080.0)], 579: [(448, 7200.0)], 591: [(448, 7200.0)], 448: [(247, 10080.0)], 247: [(475, 0.0)], 475: [], 351: [(958, 0.5)]}\nsource = 351\ntarget = 475\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27360.5, "source_answer": 27360.5}
{"source_row": 1204, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {49: [(142, 2.0)], 278: [(142, 2.0)], 142: [(52, 0.33)], 52: [(113, 0.5), (575, 1.0)], 575: [(966, 0.33)], 113: [(966, 0.33)], 966: [(497, 0.0)], 497: [], 8: [(49, 20.0), (278, 20.0)]}\nsource = 8\ntarget = 497\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.66, "source_answer": 23.66}
{"source_row": 1205, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {509: [(279, 0.17)], 279: [(548, 2.0), (592, 2.0)], 592: [(901, 0.02)], 548: [(901, 0.02)], 901: [(194, 0.0)], 194: [], 255: [(509, 1.0)]}\nsource = 255\ntarget = 194\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.19, "source_answer": 3.19}
{"source_row": 1206, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {723: [(684, 0.58)], 684: [(435, 2.0), (869, 5.0)], 869: [(914, 5.0)], 435: [(914, 5.0)], 914: [(603, 0.0)], 603: [], 429: [(723, 0.42)]}\nsource = 429\ntarget = 603\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 1207, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {827: [(758, 129600.0), (980, 129600.0)], 758: [(930, 3600.0)], 980: [(930, 3600.0)], 930: [(70, 2400.0)], 70: [(981, 960.0)], 981: [(129, 0.0)], 129: [], 824: [(827, 129600.0)]}\nsource = 824\ntarget = 129\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 266160.0, "source_answer": 266160.0}
{"source_row": 1208, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {898: [(931, 0.03)], 931: [(636, 0.02), (891, 0.02)], 891: [(480, 0.02)], 636: [(776, 0.02)], 480: [(971, 0.1)], 776: [(971, 0.1)], 971: [(943, 0.0)], 943: [], 795: [(898, 0.05)]}\nsource = 795\ntarget = 943\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.22, "source_answer": 0.22}
{"source_row": 1209, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {99: [(745, 5.0)], 218: [(689, 60.0)], 689: [(158, 120.0)], 158: [(693, 0.0)], 745: [(218, 2.0)], 394: [(745, 5.0)], 20: [(745, 5.0)], 693: [], 214: [(99, 20.0), (394, 10.0), (20, 15.0)]}\nsource = 214\ntarget = 693\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 207.0, "source_answer": 207.0}
{"source_row": 1210, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {588: [(399, 2.0)], 399: [(18, 15.0)], 18: [(415, 10.0)], 415: [(899, 5.0), (257, 3.0)], 257: [(517, 5.0)], 899: [(517, 5.0)], 517: [(8, 0.0)], 8: [], 941: [(588, 5.0)]}\nsource = 941\ntarget = 8\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 42.0, "source_answer": 42.0}
{"source_row": 1211, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {979: [(602, 1.0)], 809: [(178, 1.0)], 178: [(606, 0.58)], 606: [(603, 0.0)], 602: [(178, 1.0)], 603: [], 830: [(979, 2.0), (809, 2.0)]}\nsource = 830\ntarget = 603\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.58, "source_answer": 4.58}
{"source_row": 1212, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {27: [(236, 0.02)], 867: [(236, 0.02)], 236: [(830, 0.02)], 830: [(105, 0.02)], 105: [(883, 0.0)], 883: [], 838: [(27, 0.02), (867, 0.08)]}\nsource = 838\ntarget = 883\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.14, "source_answer": 0.14}
{"source_row": 1213, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {236: [(359, 0.02)], 359: [(651, 0.02), (635, 0.02)], 635: [(40, 0.02)], 651: [(40, 0.02)], 40: [(77, 0.0)], 77: [], 614: [(236, 0.02)]}\nsource = 614\ntarget = 77\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.08, "source_answer": 0.08}
{"source_row": 1214, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {636: [(49, 2.0), (147, 2.0)], 49: [(599, 0.0)], 147: [(599, 0.0)], 599: [], 296: [(636, 2.0)]}\nsource = 296\ntarget = 599\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1215, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {117: [(692, 10.0)], 952: [(692, 10.0)], 692: [(411, 0.0)], 411: [], 488: [(117, 1.0), (952, 1.0)]}\nsource = 488\ntarget = 411\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 1216, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(824, 1.0)], 303: [(355, 0.0)], 824: [(355, 0.0)], 355: [], 905: [(316, 5.0), (303, 2.0)]}\nsource = 905\ntarget = 355\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 1217, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {5: [(579, 0.42)], 412: [(579, 0.42)], 579: [(383, 0.0)], 383: [], 276: [(5, 0.08), (412, 0.08)]}\nsource = 276\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.5, "source_answer": 0.5}
{"source_row": 1218, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {393: [(823, 10.0)], 823: [(949, 0.0)], 293: [(949, 0.0)], 949: [], 105: [(393, 5.0), (293, 2.0)]}\nsource = 105\ntarget = 949\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1219, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {938: [(501, 60.0), (659, 60.0)], 501: [(546, 0.0)], 659: [(546, 0.0)], 546: [], 277: [(938, 30.0)]}\nsource = 277\ntarget = 546\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 1220, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {68: [(542, 0.0)], 969: [(971, 2.0), (68, 5.0)], 971: [(542, 0.0)], 542: [], 287: [(969, 10.0)]}\nsource = 287\ntarget = 542\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1221, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {672: [(38, 60.0), (2, 60.0)], 38: [(880, 0.0)], 2: [(880, 0.0)], 880: [], 256: [(672, 120.0)]}\nsource = 256\ntarget = 880\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 1222, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {740: [(786, 5.0)], 93: [(786, 5.0)], 786: [(223, 0.0)], 223: [], 782: [(740, 2.0), (93, 2.0)]}\nsource = 782\ntarget = 223\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1223, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {359: [(854, 10080.0)], 642: [(854, 10080.0)], 854: [(285, 0.0)], 285: [], 235: [(359, 1440.0), (642, 60.0)]}\nsource = 235\ntarget = 285\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11520.0, "source_answer": 11520.0}
{"source_row": 1224, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {40: [(685, 30.0)], 816: [(685, 30.0)], 685: [(676, 0.0)], 676: [], 422: [(40, 15.0), (816, 5.0)]}\nsource = 422\ntarget = 676\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1225, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {269: [(771, 2880.0)], 831: [(771, 2880.0)], 771: [(619, 0.0)], 619: [], 737: [(269, 10080.0), (831, 10080.0)]}\nsource = 737\ntarget = 619\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12960.0, "source_answer": 12960.0}
{"source_row": 1226, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {27: [(253, 0.0)], 300: [(27, 5.0)], 178: [(253, 0.0)], 253: [], 123: [(300, 10.0), (178, 2.0)]}\nsource = 123\ntarget = 253\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1227, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {327: [(727, 15.0), (47, 30.0)], 727: [(483, 0.0)], 47: [(483, 0.0)], 483: [], 763: [(327, 5.0)]}\nsource = 763\ntarget = 483\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1228, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {415: [(486, 0.0)], 97: [(415, 5.0)], 319: [(415, 5.0)], 486: [], 360: [(97, 10.0), (319, 15.0)]}\nsource = 360\ntarget = 486\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1229, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {122: [(697, 0.0)], 86: [(730, 2.0)], 730: [(697, 0.0)], 697: [], 916: [(122, 5.0), (86, 3.0)]}\nsource = 916\ntarget = 697\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 1230, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {934: [(837, 0.0)], 305: [(934, 10.0)], 483: [(934, 10.0)], 837: [], 643: [(305, 15.0), (483, 20.0)]}\nsource = 643\ntarget = 837\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1231, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {915: [(170, 1.0)], 170: [(21, 0.0)], 528: [(170, 1.0)], 21: [], 686: [(915, 2.0), (528, 2.0)]}\nsource = 686\ntarget = 21\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 1232, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {556: [(271, 0.0)], 778: [(164, 3.0)], 164: [(271, 0.0)], 271: [], 426: [(556, 2.0), (778, 2.0)]}\nsource = 426\ntarget = 271\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 1233, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {890: [(855, 20.0), (334, 20.0)], 855: [(129, 0.0)], 334: [(129, 0.0)], 129: [], 25: [(890, 10.0)]}\nsource = 25\ntarget = 129\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1234, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {390: [(624, 0.0)], 759: [(390, 30.0)], 412: [(390, 30.0)], 624: [], 487: [(759, 60.0), (412, 60.0)]}\nsource = 487\ntarget = 624\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 1235, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {780: [(294, 20160.0)], 294: [(351, 0.0)], 161: [(351, 0.0)], 351: [], 709: [(780, 10080.0), (161, 30240.0)]}\nsource = 709\ntarget = 351\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30240.0, "source_answer": 30240.0}
{"source_row": 1236, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {751: [(538, 120.0)], 521: [(541, 0.0)], 538: [(541, 0.0)], 541: [], 367: [(751, 120.0), (521, 20160.0)]}\nsource = 367\ntarget = 541\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 1237, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {337: [(886, 2.0), (742, 5.0)], 886: [(809, 0.0)], 742: [(809, 0.0)], 809: [], 111: [(337, 1440.0)]}\nsource = 111\ntarget = 809\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1445.0, "source_answer": 1445.0}
{"source_row": 1238, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {442: [(359, 0.0)], 400: [(442, 600.0)], 784: [(442, 600.0)], 359: [], 908: [(400, 2880.0), (784, 1440.0)]}\nsource = 908\ntarget = 359\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3480.0, "source_answer": 3480.0}
{"source_row": 1239, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {592: [(921, 60.0), (517, 60.0)], 921: [(91, 0.0)], 517: [(91, 0.0)], 91: [], 578: [(592, 30.0)]}\nsource = 578\ntarget = 91\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 1240, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {970: [(408, 2.0), (791, 3.0)], 408: [(665, 0.0)], 791: [(665, 0.0)], 665: [], 920: [(970, 5.0)]}\nsource = 920\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 1241, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {750: [(177, 0.0)], 396: [(177, 0.0)], 691: [(750, 30.0)], 177: [], 456: [(396, 60.0), (691, 20.0)]}\nsource = 456\ntarget = 177\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1242, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {161: [(254, 2.0)], 254: [(745, 0.0)], 322: [(745, 0.0)], 745: [], 28: [(161, 5.0), (322, 3.0)]}\nsource = 28\ntarget = 745\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1243, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {220: [(834, 10.0), (755, 20.0), (130, 10.0)], 755: [(844, 0.0)], 130: [(844, 0.0)], 834: [(844, 0.0)], 844: [], 624: [(220, 10.0)]}\nsource = 624\ntarget = 844\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1244, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {814: [(733, 1.0)], 208: [(814, 1.0)], 733: [(649, 0.0)], 254: [(814, 1.0)], 649: [], 474: [(208, 1.0), (254, 2.0)]}\nsource = 474\ntarget = 649\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1245, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {786: [(291, 60480.0), (263, 40320.0), (497, 60480.0)], 263: [(414, 0.0)], 497: [(414, 0.0)], 291: [(414, 0.0)], 414: [], 364: [(786, 129600.0)]}\nsource = 364\ntarget = 414\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 190080.0, "source_answer": 190080.0}
{"source_row": 1246, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {721: [(861, 5.0), (307, 15.0)], 307: [(865, 0.0)], 575: [(865, 0.0)], 861: [(865, 0.0)], 865: [], 402: [(721, 30.0), (575, 120.0)]}\nsource = 402\ntarget = 865\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 1247, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {449: [(187, 0.0)], 729: [(338, 10.0), (449, 5.0)], 338: [(187, 0.0)], 187: [], 372: [(729, 2.0)]}\nsource = 372\ntarget = 187\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 1248, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {17: [(753, 2.0)], 638: [(753, 2.0)], 753: [(308, 0.0)], 308: [], 244: [(17, 525600.0), (638, 60.0)]}\nsource = 244\ntarget = 308\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525602.0, "source_answer": 525602.0}
{"source_row": 1249, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {396: [(500, 2.0)], 500: [(43, 3.0)], 43: [(113, 0.0)], 583: [(43, 3.0)], 113: [], 861: [(396, 5.0), (583, 4.0)]}\nsource = 861\ntarget = 113\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 1250, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {110: [(222, 5.0), (989, 10.0), (206, 15.0)], 989: [(891, 0.0)], 206: [(891, 0.0)], 222: [(891, 0.0)], 891: [], 200: [(110, 5.0)]}\nsource = 200\ntarget = 891\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1251, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {895: [(455, 2880.0)], 344: [(901, 0.0)], 455: [(901, 0.0)], 901: [], 983: [(895, 20160.0), (344, 10080.0)]}\nsource = 983\ntarget = 901\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23040.0, "source_answer": 23040.0}
{"source_row": 1252, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {109: [(650, 0.0)], 822: [(109, 2880.0)], 561: [(650, 0.0)], 806: [(822, 86400.0), (561, 86400.0)], 650: [], 393: [(806, 20160.0)]}\nsource = 393\ntarget = 650\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 109440.0, "source_answer": 109440.0}
{"source_row": 1253, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {323: [(6, 60.0)], 6: [(472, 180.0), (801, 40320.0)], 801: [(471, 0.0)], 472: [(471, 0.0)], 471: [], 746: [(323, 30.0)]}\nsource = 746\ntarget = 471\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40410.0, "source_answer": 40410.0}
{"source_row": 1254, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {889: [(226, 2.0), (135, 2.0)], 226: [(823, 0.0)], 135: [(304, 3.0)], 304: [(823, 0.0)], 823: [], 682: [(889, 1.0)]}\nsource = 682\ntarget = 823\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 1255, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {565: [(526, 40320.0)], 526: [(795, 0.0)], 168: [(795, 0.0)], 795: [], 79: [(565, 1440.0), (168, 30240.0)]}\nsource = 79\ntarget = 795\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41760.0, "source_answer": 41760.0}
{"source_row": 1256, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {970: [(136, 20.0), (804, 30.0), (278, 45.0)], 804: [(746, 0.0)], 278: [(746, 0.0)], 136: [(746, 0.0)], 746: [], 655: [(970, 10.0)]}\nsource = 655\ntarget = 746\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1257, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {151: [(595, 259200.0), (460, 129600.0), (741, 259200.0)], 460: [(360, 0.0)], 741: [(360, 0.0)], 595: [(360, 0.0)], 360: [], 25: [(151, 20160.0)]}\nsource = 25\ntarget = 360\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 279360.0, "source_answer": 279360.0}
{"source_row": 1258, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {487: [(806, 20.0), (181, 10.0), (494, 15.0)], 181: [(611, 0.0)], 494: [(611, 0.0)], 806: [(611, 0.0)], 611: [], 294: [(487, 5.0)]}\nsource = 294\ntarget = 611\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1259, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {754: [(439, 1.0)], 342: [(439, 1.0)], 439: [(585, 0.0)], 585: [], 77: [(754, 1.0), (342, 2.0)]}\nsource = 77\ntarget = 585\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 1260, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(893, 0.0)], 832: [(893, 0.0)], 206: [(893, 0.0)], 518: [(832, 5.0), (458, 20.0), (206, 10.0)], 893: [], 57: [(518, 30.0)]}\nsource = 57\ntarget = 893\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1261, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {907: [(497, 10.0)], 983: [(497, 10.0)], 497: [(745, 2.0)], 745: [(476, 0.0)], 476: [], 426: [(907, 2.0), (983, 5.0)]}\nsource = 426\ntarget = 476\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 1262, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {209: [(243, 60.0)], 428: [(243, 60.0)], 544: [(243, 60.0)], 243: [(512, 0.0)], 512: [], 913: [(209, 30.0), (428, 15.0), (544, 120.0)]}\nsource = 913\ntarget = 512\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 1263, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {242: [(324, 20.0), (713, 15.0), (767, 30.0)], 713: [(666, 0.0)], 767: [(666, 0.0)], 324: [(666, 0.0)], 666: [], 133: [(242, 120.0)]}\nsource = 133\ntarget = 666\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 1264, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {371: [(482, 2.0)], 482: [(990, 0.0)], 427: [(371, 2.0)], 312: [(990, 0.0)], 990: [], 939: [(427, 3.0), (312, 1.0)]}\nsource = 939\ntarget = 990\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1265, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {382: [(865, 0.0)], 174: [(650, 30.0)], 847: [(865, 0.0)], 650: [(847, 15.0)], 865: [], 985: [(382, 30.0), (174, 10.0)]}\nsource = 985\ntarget = 865\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1266, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(361, 2.0)], 41: [(361, 2.0)], 361: [(664, 2.0)], 664: [(79, 0.0)], 79: [], 509: [(589, 5.0), (41, 2.0)]}\nsource = 509\ntarget = 79\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1267, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {119: [(979, 0.0)], 602: [(979, 0.0)], 937: [(979, 0.0)], 927: [(119, 120.0), (937, 180.0)], 979: [], 224: [(602, 240.0), (927, 60.0)]}\nsource = 224\ntarget = 979\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0}
{"source_row": 1268, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {531: [(807, 2.0), (415, 60.0)], 274: [(531, 10.0)], 415: [(851, 0.0)], 807: [(851, 0.0)], 851: [], 765: [(274, 15.0)]}\nsource = 765\ntarget = 851\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 1269, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {131: [(321, 30.0), (781, 15.0), (333, 15.0)], 781: [(254, 0.0)], 333: [(254, 0.0)], 321: [(254, 0.0)], 254: [], 57: [(131, 30.0)]}\nsource = 57\ntarget = 254\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0}
{"source_row": 1270, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {482: [(959, 10080.0), (62, 5.0), (698, 30.0)], 62: [(131, 0.0)], 698: [(131, 0.0)], 959: [(131, 0.0)], 131: [], 917: [(482, 60.0)]}\nsource = 917\ntarget = 131\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10140.0, "source_answer": 10140.0}
{"source_row": 1271, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {71: [(628, 0.0)], 575: [(628, 0.0)], 677: [(628, 0.0)], 642: [(71, 15.0)], 628: [], 812: [(575, 1.0), (677, 5.0), (642, 10.0)]}\nsource = 812\ntarget = 628\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1272, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {623: [(877, 0.0)], 871: [(112, 5.0)], 112: [(877, 0.0)], 85: [(877, 0.0)], 877: [], 777: [(623, 15.0), (871, 15.0), (85, 2.0)]}\nsource = 777\ntarget = 877\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1273, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {101: [(422, 0.0)], 184: [(422, 0.0)], 180: [(184, 15.0), (101, 10.0)], 422: [], 686: [(180, 5.0)]}\nsource = 686\ntarget = 422\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1274, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {815: [(767, 180.0), (69, 120.0)], 767: [(908, 0.0)], 69: [(908, 0.0)], 264: [(908, 0.0)], 121: [(908, 0.0)], 908: [], 462: [(815, 2880.0), (264, 120.0), (121, 4320.0)]}\nsource = 462\ntarget = 908\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4320.0, "source_answer": 4320.0}
{"source_row": 1275, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {866: [(939, 15.0)], 939: [(697, 30.0)], 697: [(248, 0.0)], 506: [(939, 15.0)], 248: [], 73: [(866, 5.0), (506, 5.0)]}\nsource = 73\ntarget = 248\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1276, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {770: [(911, 1.0)], 911: [(857, 1.0)], 162: [(857, 1.0)], 857: [(709, 1.0)], 709: [(571, 0.0)], 571: [], 475: [(770, 2.0), (162, 2.0)]}\nsource = 475\ntarget = 571\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 1277, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {955: [(2, 6.0), (362, 3.0), (402, 5.0), (857, 4.0)], 402: [(822, 0.0)], 857: [(822, 0.0)], 2: [(822, 0.0)], 362: [(822, 0.0)], 822: [], 888: [(955, 5.0)]}\nsource = 888\ntarget = 822\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 1278, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {214: [(851, 60.0)], 994: [(944, 30.0)], 944: [(218, 0.0)], 851: [(341, 15.0)], 341: [(218, 0.0)], 218: [], 496: [(214, 120.0), (994, 60.0)]}\nsource = 496\ntarget = 218\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 195.0, "source_answer": 195.0}
{"source_row": 1279, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {440: [(264, 60.0), (368, 40.0), (335, 60.0), (476, 45.0)], 335: [(726, 0.0)], 476: [(726, 0.0)], 264: [(726, 0.0)], 368: [(726, 0.0)], 726: [], 384: [(440, 120.0)]}\nsource = 384\ntarget = 726\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 1280, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {141: [(539, 0.0)], 877: [(539, 0.0)], 105: [(539, 0.0)], 605: [(877, 120.0), (141, 60.0), (105, 30.0)], 539: [], 904: [(605, 15.0)]}\nsource = 904\ntarget = 539\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 1281, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {549: [(254, 3.0)], 254: [(711, 1.0)], 711: [(575, 2.0)], 665: [(711, 1.0)], 575: [(864, 0.0)], 864: [], 335: [(549, 5.0), (665, 10.0)]}\nsource = 335\ntarget = 864\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0}
{"source_row": 1282, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {558: [(994, 20160.0), (197, 80640.0)], 994: [(433, 0.0)], 197: [(433, 0.0)], 433: [], 851: [(558, 10080.0)]}\nsource = 851\ntarget = 433\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90720.0, "source_answer": 90720.0}
{"source_row": 1283, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {939: [(888, 1440.0)], 585: [(888, 1440.0)], 479: [(888, 1440.0)], 888: [(867, 0.0)], 867: [], 719: [(939, 4320.0), (585, 2880.0), (479, 30240.0)]}\nsource = 719\ntarget = 867\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31680.0, "source_answer": 31680.0}
{"source_row": 1284, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {254: [(602, 0.0)], 144: [(184, 30.0)], 184: [(174, 20.0)], 174: [(156, 25.0)], 156: [(602, 0.0)], 602: [], 688: [(254, 10.0), (144, 10.0)]}\nsource = 688\ntarget = 602\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 1285, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {380: [(985, 15.0)], 985: [(468, 20.0)], 468: [(772, 0.0)], 997: [(772, 0.0)], 754: [(772, 0.0)], 772: [], 693: [(380, 10.0), (997, 10.0), (754, 1.0)]}\nsource = 693\ntarget = 772\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1286, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {193: [(729, 0.0)], 539: [(4, 6.0)], 177: [(4, 6.0)], 4: [(895, 10.0)], 895: [(729, 0.0)], 729: [], 355: [(193, 5.0), (539, 2.0), (177, 4.0)]}\nsource = 355\ntarget = 729\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1287, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {501: [(614, 3.0)], 614: [(334, 1.0)], 334: [(165, 5.0)], 280: [(334, 1.0)], 165: [(27, 0.0)], 27: [], 580: [(501, 2.0), (280, 5.0)]}\nsource = 580\ntarget = 27\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0}
{"source_row": 1288, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {998: [(668, 60.0)], 668: [(53, 10.0)], 53: [(13, 518400.0)], 291: [(53, 10.0)], 13: [(282, 0.0)], 282: [], 375: [(998, 5.0), (291, 1.0)]}\nsource = 375\ntarget = 282\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 518475.0, "source_answer": 518475.0}
{"source_row": 1289, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {698: [(658, 10.0)], 658: [(907, 120.0)], 907: [(186, 0.0)], 747: [(186, 0.0)], 186: [], 239: [(698, 5.0), (747, 30.0)]}\nsource = 239\ntarget = 186\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0}
{"source_row": 1290, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {206: [(875, 2.0)], 875: [(50, 3.0), (931, 2.0), (308, 1.0)], 308: [(287, 0.0)], 50: [(287, 0.0)], 931: [(287, 0.0)], 287: [], 525: [(206, 5.0)]}\nsource = 525\ntarget = 287\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0}
{"source_row": 1291, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {823: [(243, 0.0)], 85: [(847, 10.0)], 847: [(243, 0.0)], 243: [], 875: [(823, 5.0), (85, 15.0)]}\nsource = 875\ntarget = 243\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1292, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {335: [(693, 10.0)], 693: [(318, 20.0)], 225: [(318, 20.0)], 318: [(36, 1440.0)], 36: [(657, 0.0)], 657: [], 681: [(335, 10.0), (225, 15.0)]}\nsource = 681\ntarget = 657\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1480.0, "source_answer": 1480.0}
{"source_row": 1293, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {743: [(79, 0.0)], 291: [(237, 10.0)], 237: [(685, 5.0)], 685: [(724, 15.0)], 724: [(79, 0.0)], 79: [], 553: [(743, 30.0), (291, 5.0)]}\nsource = 553\ntarget = 79\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1294, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {948: [(923, 15.0), (123, 20.0)], 923: [(38, 45.0)], 123: [(38, 45.0)], 38: [(777, 20160.0)], 777: [(488, 0.0)], 488: [], 49: [(948, 30.0)]}\nsource = 49\ntarget = 488\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20255.0, "source_answer": 20255.0}
{"source_row": 1295, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {848: [(496, 2.0)], 496: [(649, 10.0)], 520: [(649, 10.0)], 649: [(30, 0.0)], 471: [(30, 0.0)], 30: [], 798: [(848, 10.0), (520, 1.0), (471, 10.0)]}\nsource = 798\ntarget = 30\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0}
{"source_row": 1296, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {382: [(148, 120.0), (118, 120.0), (284, 60.0), (339, 240.0)], 284: [(760, 0.0)], 339: [(760, 0.0)], 148: [(760, 0.0)], 118: [(760, 0.0)], 760: [], 674: [(382, 120.0)]}\nsource = 674\ntarget = 760\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 360.0, "source_answer": 360.0}
{"source_row": 1297, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {147: [(392, 3.0)], 51: [(392, 3.0)], 392: [(869, 1.0)], 869: [(850, 12.0)], 850: [(215, 0.0)], 215: [], 574: [(147, 2.0), (51, 2.0)]}\nsource = 574\ntarget = 215\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0}
{"source_row": 1298, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {188: [(963, 2.0)], 828: [(963, 2.0)], 777: [(963, 2.0)], 174: [(963, 2.0)], 963: [(118, 0.0)], 118: [], 899: [(188, 5.0), (828, 10.0), (777, 3.0), (174, 5.0)]}\nsource = 899\ntarget = 118\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0}
{"source_row": 1299, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {29: [(282, 1.0), (706, 1.0), (583, 3.0), (587, 1.0)], 583: [(597, 0.0)], 587: [(597, 0.0)], 282: [(597, 0.0)], 706: [(597, 0.0)], 597: [], 294: [(29, 2.0)]}\nsource = 294\ntarget = 597\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0}
{"source_row": 1300, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {406: [(674, 0.0)], 660: [(674, 0.0)], 779: [(674, 0.0)], 967: [(674, 0.0)], 301: [(967, 2.0), (406, 2.0), (660, 2.0), (779, 2.0)], 674: [], 163: [(301, 1.0)]}\nsource = 163\ntarget = 674\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0}
{"source_row": 1301, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {437: [(79, 15.0)], 619: [(874, 0.0)], 79: [(874, 0.0)], 874: [], 295: [(437, 5.0), (619, 2.0)]}\nsource = 295\ntarget = 874\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1302, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(365, 120.0)], 365: [(731, 0.0)], 443: [(365, 120.0)], 543: [(365, 120.0)], 731: [], 480: [(863, 60.0), (443, 180.0), (543, 30.0)]}\nsource = 480\ntarget = 731\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0}
{"source_row": 1303, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {620: [(676, 0.0)], 968: [(878, 5.0)], 878: [(676, 0.0)], 676: [], 923: [(620, 3.0), (968, 2.0)]}\nsource = 923\ntarget = 676\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0}
{"source_row": 1304, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {295: [(420, 10080.0)], 175: [(760, 0.0)], 420: [(760, 0.0)], 760: [], 488: [(295, 60.0), (175, 60.0)]}\nsource = 488\ntarget = 760\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10140.0, "source_answer": 10140.0}
{"source_row": 1305, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {192: [(514, 60.0)], 514: [(557, 0.0)], 200: [(557, 0.0)], 557: [], 938: [(192, 30.0), (200, 15.0)]}\nsource = 938\ntarget = 557\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 1306, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {806: [(479, 20.0)], 321: [(6, 5.0)], 6: [(479, 20.0)], 479: [(963, 10.0)], 963: [(270, 0.0)], 270: [], 249: [(806, 10.0), (321, 20.0)]}\nsource = 249\ntarget = 270\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1307, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {240: [(61, 2.0)], 61: [(206, 5.0)], 206: [(548, 10.0)], 919: [(548, 10.0)], 548: [(702, 0.0)], 702: [], 265: [(240, 15.0), (919, 15.0)]}\nsource = 265\ntarget = 702\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 1308, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {297: [(186, 5.0)], 186: [(741, 15.0), (920, 10.0)], 920: [(876, 20.0)], 741: [(876, 20.0)], 876: [(790, 0.0)], 790: [], 499: [(297, 15.0)]}\nsource = 499\ntarget = 790\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1309, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {981: [(320, 0.0)], 627: [(981, 30.0)], 902: [(320, 0.0)], 93: [(320, 0.0)], 938: [(320, 0.0)], 320: [], 966: [(627, 60.0), (902, 20.0), (93, 45.0), (938, 60.0)]}\nsource = 966\ntarget = 320\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0}
{"source_row": 1310, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {618: [(15, 40.0)], 544: [(15, 40.0)], 46: [(15, 40.0)], 15: [(627, 0.0)], 627: [], 424: [(618, 5.0), (544, 10.0), (46, 5.0)]}\nsource = 424\ntarget = 627\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0}
{"source_row": 1311, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {110: [(270, 25.0)], 30: [(489, 15.0)], 489: [(411, 5.0)], 411: [(270, 25.0)], 270: [(395, 0.0)], 395: [], 760: [(110, 10.0), (30, 10.0)]}\nsource = 760\ntarget = 395\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1312, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {589: [(955, 2.0)], 483: [(622, 1.0)], 622: [(210, 1.0)], 210: [(653, 0.0)], 955: [(653, 0.0)], 653: [], 469: [(589, 2.0), (483, 1.0)]}\nsource = 469\ntarget = 653\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1313, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {738: [(735, 0.0)], 464: [(735, 0.0)], 241: [(735, 0.0)], 468: [(738, 10.0)], 631: [(738, 10.0)], 735: [], 100: [(464, 5.0), (241, 3.0), (468, 2.0), (631, 5.0)]}\nsource = 100\ntarget = 735\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1314, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {458: [(769, 0.0)], 554: [(32, 15.0)], 32: [(769, 0.0)], 91: [(769, 0.0)], 193: [(769, 0.0)], 769: [], 92: [(458, 5.0), (554, 10.0), (91, 15.0), (193, 20.0)]}\nsource = 92\ntarget = 769\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1315, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {428: [(527, 5.0)], 951: [(527, 5.0)], 454: [(527, 5.0)], 527: [(166, 0.0)], 166: [], 695: [(428, 30.0), (951, 15.0), (454, 10.0)]}\nsource = 695\ntarget = 166\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1316, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {346: [(827, 5.0)], 523: [(827, 5.0)], 69: [(827, 5.0)], 492: [(827, 5.0)], 827: [(767, 0.0)], 767: [], 414: [(346, 2.0), (523, 3.0), (69, 10.0), (492, 30.0)]}\nsource = 414\ntarget = 767\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1317, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {910: [(907, 10.0), (839, 15.0)], 907: [(364, 0.0)], 839: [(364, 0.0)], 364: [], 614: [(910, 5.0)]}\nsource = 614\ntarget = 364\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1318, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {783: [(958, 0.0)], 443: [(958, 0.0)], 778: [(958, 0.0)], 866: [(783, 60.0), (778, 2880.0)], 958: [], 478: [(443, 20160.0), (866, 20160.0)]}\nsource = 478\ntarget = 958\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23040.0, "source_answer": 23040.0}
{"source_row": 1319, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {876: [(161, 30.0)], 852: [(161, 30.0)], 161: [(632, 0.0)], 632: [], 807: [(876, 1440.0), (852, 60.0)]}\nsource = 807\ntarget = 632\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1470.0, "source_answer": 1470.0}
{"source_row": 1320, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {953: [(592, 1.0), (336, 1.0)], 592: [(276, 1.0)], 336: [(797, 1.0)], 797: [(276, 1.0)], 276: [(366, 0.0)], 119: [(366, 0.0)], 366: [], 353: [(953, 1.0), (119, 1.0)]}\nsource = 353\ntarget = 366\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1321, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {469: [(678, 3.0)], 678: [(42, 1.0)], 292: [(42, 1.0)], 42: [(753, 5.0)], 753: [(580, 15.0)], 580: [(17, 0.0)], 17: [], 550: [(469, 5.0), (292, 1.0)]}\nsource = 550\ntarget = 17\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 29.0, "source_answer": 29.0}
{"source_row": 1322, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {37: [(839, 2.0)], 839: [(418, 2.0)], 912: [(717, 5.0)], 418: [(912, 3.0)], 717: [(745, 0.0)], 691: [(745, 0.0)], 745: [], 948: [(37, 5.0), (691, 1.0)]}\nsource = 948\ntarget = 745\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 1323, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {369: [(727, 2.0)], 727: [(232, 30.0)], 111: [(232, 30.0)], 232: [(894, 360.0)], 894: [(837, 0.0)], 837: [], 384: [(369, 5.0), (111, 1.0)]}\nsource = 384\ntarget = 837\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 397.0, "source_answer": 397.0}
{"source_row": 1324, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {563: [(427, 0.0)], 810: [(427, 0.0)], 880: [(427, 0.0)], 35: [(810, 15.0), (563, 10.0), (880, 10.0)], 427: [], 425: [(35, 20.0)]}\nsource = 425\ntarget = 427\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0}
{"source_row": 1325, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {883: [(520, 20160.0), (204, 20160.0)], 520: [(838, 0.0)], 204: [(838, 0.0)], 838: [], 299: [(883, 20160.0)]}\nsource = 299\ntarget = 838\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40320.0, "source_answer": 40320.0}
{"source_row": 1326, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {242: [(639, 5.0)], 639: [(850, 0.0)], 15: [(850, 0.0)], 850: [], 741: [(242, 10.0), (15, 15.0)]}\nsource = 741\ntarget = 850\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1327, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {519: [(81, 3.0)], 342: [(81, 3.0)], 81: [(728, 5.0)], 728: [(77, 10.0)], 77: [(814, 15.0)], 814: [(328, 0.0)], 328: [], 390: [(519, 5.0), (342, 2.0)]}\nsource = 390\ntarget = 328\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 38.0, "source_answer": 38.0}
{"source_row": 1328, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {918: [(364, 10.0)], 364: [(475, 3.0), (842, 3.0)], 475: [(420, 2.0)], 420: [(674, 1.0)], 842: [(420, 2.0)], 674: [(747, 0.0)], 747: [], 306: [(918, 5.0)]}\nsource = 306\ntarget = 747\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0}
{"source_row": 1329, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {998: [(26, 20160.0)], 26: [(861, 86400.0)], 861: [(320, 0.0)], 734: [(26, 20160.0)], 320: [], 891: [(998, 129600.0), (734, 2102400.0)]}\nsource = 891\ntarget = 320\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2208960.0, "source_answer": 2208960.0}
{"source_row": 1330, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {390: [(732, 1.0), (278, 1.0), (383, 1.0)], 278: [(660, 2.0)], 383: [(660, 2.0)], 732: [(660, 2.0)], 660: [(985, 1.0)], 985: [(853, 0.0)], 853: [], 601: [(390, 5.0)]}\nsource = 601\ntarget = 853\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1331, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {330: [(155, 5.0), (369, 10.0), (16, 5.0), (829, 5.0)], 16: [(32, 0.0)], 829: [(32, 0.0)], 155: [(32, 0.0)], 369: [(32, 0.0)], 32: [], 896: [(330, 15.0)]}\nsource = 896\ntarget = 32\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0}
{"source_row": 1332, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {399: [(506, 10.0)], 304: [(506, 10.0)], 506: [(612, 30.0)], 612: [(464, 5.0)], 464: [(717, 2.0)], 717: [(761, 0.0)], 761: [], 623: [(399, 5.0), (304, 2.0)]}\nsource = 623\ntarget = 761\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 52.0, "source_answer": 52.0}
{"source_row": 1333, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {30: [(486, 1.0)], 39: [(486, 1.0)], 486: [(499, 0.0)], 499: [], 105: [(30, 2.0), (39, 5.0)]}\nsource = 105\ntarget = 499\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0}
{"source_row": 1334, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {205: [(751, 5.0)], 751: [(785, 2.0)], 785: [(856, 15.0)], 856: [(9, 5.0)], 9: [(899, 0.0)], 936: [(899, 0.0)], 899: [], 764: [(205, 3.0), (936, 1.0)]}\nsource = 764\ntarget = 899\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0}
{"source_row": 1335, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {705: [(882, 0.0)], 186: [(882, 0.0)], 647: [(417, 15.0)], 719: [(882, 0.0)], 261: [(417, 15.0)], 417: [(882, 0.0)], 882: [], 801: [(705, 0.5), (186, 10.0), (647, 30.0), (719, 5.0), (261, 2.0)]}\nsource = 801\ntarget = 882\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1336, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {702: [(24, 2.0)], 617: [(24, 2.0)], 24: [(262, 0.0)], 947: [(992, 2.0)], 992: [(467, 5.0)], 467: [(617, 5.0), (702, 10.0)], 262: [], 514: [(947, 10.0)]}\nsource = 514\ntarget = 262\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 29.0, "source_answer": 29.0}
{"source_row": 1337, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {25: [(204, 60.0)], 967: [(204, 60.0)], 302: [(204, 60.0)], 210: [(204, 60.0)], 204: [(737, 0.0)], 737: [], 843: [(25, 30.0), (967, 60.0), (302, 15.0), (210, 30.0)]}\nsource = 843\ntarget = 737\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 1338, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {747: [(340, 1.0)], 676: [(340, 1.0)], 694: [(340, 1.0)], 492: [(340, 1.0)], 93: [(340, 1.0)], 340: [(155, 0.0)], 155: [], 115: [(747, 0.17), (676, 0.08), (694, 0.05), (492, 0.05), (93, 0.17)]}\nsource = 115\ntarget = 155\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.17, "source_answer": 1.17}
{"source_row": 1339, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {540: [(586, 2.0)], 586: [(152, 30.0), (165, 3.0)], 165: [(367, 0.0)], 152: [(400, 2.0)], 400: [(367, 0.0)], 367: [], 677: [(540, 5.0)]}\nsource = 677\ntarget = 367\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 39.0, "source_answer": 39.0}
{"source_row": 1340, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {372: [(679, 0.07), (545, 0.07), (40, 0.07), (478, 0.07)], 545: [(420, 0.03)], 40: [(420, 0.03)], 679: [(420, 0.03)], 478: [(420, 0.03)], 420: [(673, 0.03)], 673: [(479, 0.0)], 479: [], 654: [(372, 0.03)]}\nsource = 654\ntarget = 479\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.16, "source_answer": 0.16}
{"source_row": 1341, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {389: [(781, 0.0)], 201: [(389, 2.0)], 247: [(389, 2.0)], 781: [], 961: [(201, 10.0), (247, 15.0)]}\nsource = 961\ntarget = 781\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 1342, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {531: [(745, 0.0)], 540: [(745, 0.0)], 44: [(745, 0.0)], 305: [(745, 0.0)], 656: [(745, 0.0)], 318: [(745, 0.0)], 885: [(305, 5.0), (318, 5.0), (531, 5.0), (540, 5.0), (44, 5.0), (656, 5.0)], 745: [], 686: [(885, 10.0)]}\nsource = 686\ntarget = 745\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1343, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {323: [(57, 30.0), (955, 30.0), (474, 45.0)], 955: [(546, 0.0)], 474: [(546, 0.0)], 57: [(546, 0.0)], 546: [], 328: [(323, 60.0)]}\nsource = 328\ntarget = 546\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0}
{"source_row": 1344, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {257: [(919, 0.5), (889, 0.5), (715, 0.5), (16, 0.5)], 715: [(134, 0.0)], 16: [(134, 0.0)], 919: [(134, 0.0)], 889: [(134, 0.0)], 134: [], 552: [(257, 0.5)]}\nsource = 552\ntarget = 134\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.0, "source_answer": 1.0}
{"source_row": 1345, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {221: [(463, 2.0)], 528: [(595, 3.0)], 595: [(127, 3.0)], 463: [(127, 3.0)], 127: [(198, 5.0)], 813: [(198, 5.0)], 198: [(467, 0.0)], 467: [], 226: [(221, 5.0), (528, 2.0), (813, 10.0)]}\nsource = 226\ntarget = 467\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1346, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {331: [(323, 30.0)], 323: [(953, 0.0)], 118: [(953, 0.0)], 953: [], 513: [(331, 10.0), (118, 20160.0)]}\nsource = 513\ntarget = 953\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20160.0, "source_answer": 20160.0}
{"source_row": 1347, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {226: [(755, 20160.0)], 265: [(755, 20160.0)], 645: [(226, 86400.0)], 755: [(776, 0.0)], 776: [], 88: [(265, 20160.0), (645, 86400.0)]}\nsource = 88\ntarget = 776\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 192960.0, "source_answer": 192960.0}
{"source_row": 1348, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {303: [(698, 60.0)], 711: [(303, 15.0)], 535: [(698, 60.0)], 983: [(655, 15.0)], 698: [(983, 30.0)], 655: [(440, 20160.0)], 440: [(775, 0.0)], 775: [], 452: [(711, 30.0), (535, 60.0)]}\nsource = 452\ntarget = 775\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20325.0, "source_answer": 20325.0}
{"source_row": 1349, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {300: [(493, 10.0)], 493: [(349, 20.0)], 349: [(296, 5.0), (219, 10.0)], 296: [(353, 0.0)], 219: [(842, 2.0)], 842: [(353, 0.0)], 353: [], 24: [(300, 10.0)]}\nsource = 24\ntarget = 353\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 52.0, "source_answer": 52.0}
{"source_row": 1350, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {568: [(15, 0.0)], 283: [(145, 60.0)], 652: [(15, 0.0)], 145: [(15, 0.0)], 936: [(15, 0.0)], 511: [(15, 0.0)], 207: [(15, 0.0)], 15: [], 269: [(568, 30.0), (283, 60.0), (652, 30.0), (936, 30.0), (511, 30.0), (207, 15.0)]}\nsource = 269\ntarget = 15\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0}
{"source_row": 1351, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {202: [(436, 1.0), (232, 1.0)], 436: [(378, 2.0)], 232: [(378, 2.0)], 378: [(527, 5.0)], 527: [(909, 3.0)], 909: [(222, 3.0)], 222: [(777, 0.0)], 777: [], 472: [(202, 5.0)]}\nsource = 472\ntarget = 777\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 19.0, "source_answer": 19.0}
{"source_row": 1352, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {800: [(192, 20.0)], 343: [(986, 5.0)], 547: [(986, 5.0)], 986: [(860, 0.0)], 192: [(860, 0.0)], 860: [], 999: [(800, 60.0), (343, 10.0), (547, 15.0)]}\nsource = 999\ntarget = 860\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0}
{"source_row": 1353, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {621: [(103, 3.0)], 103: [(116, 2.0), (89, 1.0)], 89: [(343, 1.0)], 116: [(343, 1.0)], 343: [(255, 0.0)], 320: [(255, 0.0)], 255: [], 354: [(621, 2.0), (320, 2.0)]}\nsource = 354\ntarget = 255\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 1354, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {463: [(811, 0.0)], 157: [(98, 30.0), (748, 120.0)], 748: [(811, 0.0)], 98: [(811, 0.0)], 65: [(811, 0.0)], 723: [(811, 0.0)], 609: [(811, 0.0)], 811: [], 802: [(463, 30.0), (157, 60.0), (65, 15.0), (723, 60.0), (609, 20.0)]}\nsource = 802\ntarget = 811\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0}
{"source_row": 1355, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {443: [(820, 15.0)], 820: [(166, 15.0), (318, 30.0), (586, 20.0)], 586: [(325, 0.0)], 166: [(325, 0.0)], 318: [(325, 0.0)], 325: [], 343: [(443, 10.0)]}\nsource = 343\ntarget = 325\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0}
{"source_row": 1356, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {335: [(48, 15.0)], 716: [(446, 5.0)], 210: [(634, 5.0)], 634: [(446, 5.0)], 446: [(48, 15.0)], 48: [(731, 2.0)], 731: [(131, 0.0)], 131: [], 467: [(335, 5.0), (716, 10.0), (210, 2.0)]}\nsource = 467\ntarget = 131\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0}
{"source_row": 1357, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {685: [(506, 3.0)], 129: [(506, 3.0)], 506: [(109, 10.0)], 109: [(430, 2.0)], 430: [(900, 0.0)], 900: [], 264: [(685, 2.0), (129, 5.0)]}\nsource = 264\ntarget = 900\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0}
{"source_row": 1358, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {561: [(746, 20.0)], 94: [(208, 15.0)], 208: [(746, 20.0)], 746: [(702, 0.0)], 702: [], 488: [(561, 10.0), (94, 10.0)]}\nsource = 488\ntarget = 702\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1359, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {684: [(366, 30.0)], 792: [(938, 30.0)], 219: [(643, 15.0)], 938: [(219, 15.0)], 643: [(366, 30.0)], 366: [(230, 15.0)], 230: [(660, 0.0)], 660: [], 223: [(684, 30.0), (792, 5.0)]}\nsource = 223\ntarget = 660\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 110.0, "source_answer": 110.0}
{"source_row": 1360, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {634: [(858, 1.0)], 858: [(962, 1.0)], 962: [(597, 0.0)], 65: [(496, 1.0)], 496: [(636, 1.0)], 636: [(634, 1.0)], 856: [(65, 1.0)], 809: [(65, 1.0)], 597: [], 308: [(856, 1.0), (809, 2.0)]}\nsource = 308\ntarget = 597\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0}
{"source_row": 1361, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {318: [(216, 10.0)], 216: [(345, 2.0)], 403: [(345, 2.0)], 345: [(390, 0.0)], 390: [], 386: [(318, 5.0), (403, 2.0)]}\nsource = 386\ntarget = 390\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}
{"source_row": 1362, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {684: [(419, 20.0), (521, 20.0), (206, 30.0), (97, 30.0)], 206: [(654, 0.0)], 97: [(654, 0.0)], 419: [(654, 0.0)], 521: [(654, 0.0)], 654: [], 610: [(684, 15.0)]}\nsource = 610\ntarget = 654\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0}
{"source_row": 1363, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {875: [(929, 2.0)], 409: [(457, 10.0), (955, 10.0)], 929: [(409, 2.0)], 955: [(292, 5.0)], 457: [(292, 5.0)], 922: [(44, 15.0)], 292: [(922, 10.0)], 44: [(195, 5.0)], 195: [(609, 0.0)], 609: [], 710: [(875, 5.0)]}\nsource = 710\ntarget = 609\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 54.0, "source_answer": 54.0}
{"source_row": 1364, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {895: [(521, 60.0)], 716: [(389, 5.0)], 651: [(389, 5.0)], 389: [(892, 2.0)], 892: [(764, 2.0), (25, 3.0)], 25: [(521, 60.0)], 764: [(521, 60.0)], 521: [(992, 0.0)], 992: [(73, 0.0)], 73: [], 557: [(895, 5.0), (716, 15.0), (651, 10.0)]}\nsource = 557\ntarget = 73\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0}
{"source_row": 1365, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {716: [(470, 2.0)], 470: [(442, 2.0), (682, 3.0), (286, 3.0), (154, 2.0)], 682: [(832, 0.0)], 442: [(832, 0.0)], 154: [(832, 0.0)], 286: [(832, 0.0)], 20: [(716, 1.0)], 505: [(716, 1.0)], 833: [(716, 1.0)], 832: [], 472: [(20, 2.0), (505, 3.0), (833, 2.0)]}\nsource = 472\ntarget = 832\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0}
{"source_row": 1366, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {1: [(59, 2.0)], 59: [(566, 0.0)], 794: [(566, 0.0)], 808: [(566, 0.0)], 646: [(808, 2.0)], 565: [(566, 0.0)], 408: [(566, 0.0)], 566: [], 797: [(1, 1.0), (794, 1.0), (646, 2.0), (565, 1.0), (408, 2.0)]}\nsource = 797\ntarget = 566\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.0, "source_answer": 4.0}
{"source_row": 1367, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {316: [(48, 10.0)], 224: [(48, 10.0)], 48: [(408, 0.0)], 408: [], 386: [(316, 5.0), (224, 2.0)]}\nsource = 386\ntarget = 408\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0}
{"source_row": 1368, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {823: [(293, 10.0)], 293: [(990, 15.0)], 935: [(990, 15.0)], 990: [(735, 120.0)], 735: [(26, 0.0)], 26: [], 741: [(823, 5.0), (935, 2.0)]}\nsource = 741\ntarget = 26\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0}
{"source_row": 1369, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {91: [(865, 5.0), (146, 2.0)], 523: [(524, 0.0)], 132: [(865, 5.0), (146, 2.0)], 762: [(865, 5.0), (146, 2.0)], 865: [(826, 10.0)], 146: [(883, 5.0)], 883: [(912, 5.0)], 826: [(883, 5.0)], 912: [(23, 5.0)], 23: [(523, 240.0)], 524: [], 180: [(91, 5.0), (132, 10.0), (762, 15.0)]}\nsource = 180\ntarget = 524\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 285.0, "source_answer": 285.0}
{"source_row": 1370, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {871: [(213, 10.0), (76, 1440.0)], 213: [(885, 0.0)], 76: [(885, 0.0)], 885: [], 884: [(871, 5.0)]}\nsource = 884\ntarget = 885\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1445.0, "source_answer": 1445.0}
{"source_row": 1371, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {594: [(206, 2.0)], 314: [(722, 4.0)], 803: [(67, 0.0)], 722: [(803, 2.0), (964, 2.0)], 964: [(67, 0.0)], 630: [(206, 2.0)], 896: [(136, 3.0)], 206: [(136, 3.0)], 136: [(721, 2.0)], 721: [(756, 2.0)], 756: [(345, 5.0)], 345: [(416, 2.0)], 416: [(314, 10.0)], 67: [], 710: [(594, 5.0), (630, 5.0), (896, 5.0)]}\nsource = 710\ntarget = 67\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0}
{"source_row": 1372, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n    G = nx.DiGraph()\n    for src, neighbors in weighted_adj_list.items():\n        for tgt, weight in neighbors:\n            G.add_edge(src, tgt, weight=weight)\n\n    topo_order = list(nx.topological_sort(G))\n    dist = {node: float('-inf') for node in G.nodes}\n    pred = {node: None for node in G.nodes}\n    dist[source] = 0\n\n    for u in topo_order:\n        for v in G.successors(u):\n            weight = G[u][v]['weight']\n            if dist[u] + weight > dist[v]:\n                dist[v] = dist[u] + weight\n                pred[v] = u\n\n    if dist[target] == float('-inf'):\n        return None, []\n\n    path = []\n    current = target\n    while current is not None:\n        path.append(current)\n        current = pred[current]\n    path.reverse()\n\n    return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {931: [(76, 1.0)], 993: [(855, 2.0)], 855: [(571, 1.0)], 571: [(652, 2.0)], 652: [(814, 3.0)], 814: [(900, 0.0)], 886: [(76, 1.0)], 440: [(76, 1.0)], 76: [(420, 2.0)], 420: [(486, 2.0), (879, 2.0), (965, 2.0), (303, 1.0)], 486: [(993, 2.0)], 879: [(993, 2.0)], 965: [(993, 2.0)], 303: [(993, 2.0)], 900: [], 24: [(931, 2.0), (886, 2.0), (440, 1.0)]}\nsource = 24\ntarget = 900\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0}