Datasets:
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9ffd7d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | {"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 = {997: [(192, 30.0)], 192: [(725, 5.0), (292, 5.0)], 292: [(88, 20.0)], 725: [(88, 20.0)], 88: [(4, 15.0)], 4: [(128, 0.0)], 128: [], 506: [(997, 10.0)]}\nsource = 506\ntarget = 128\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": 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 = {321: [(247, 5.0)], 676: [(247, 5.0)], 247: [(725, 10.0), (59, 5.0)], 725: [(875, 60.0)], 59: [(875, 60.0)], 875: [(263, 10.0)], 263: [(57, 0.0)], 57: [], 520: [(321, 5.0), (676, 5.0)]}\nsource = 520\ntarget = 57\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": 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 = {322: [(587, 5.0), (537, 15.0)], 587: [(412, 5.0)], 537: [(412, 5.0)], 412: [(890, 60.0)], 890: [(849, 0.0)], 849: [], 9: [(322, 5.0)]}\nsource = 9\ntarget = 849\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": 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 = {694: [(234, 2.0)], 234: [(673, 1.0)], 673: [(274, 4.0), (516, 5.0)], 274: [(394, 5.0)], 516: [(394, 5.0)], 394: [(291, 525600.0)], 291: [(191, 0.0)], 191: [], 744: [(694, 1.0)]}\nsource = 744\ntarget = 191\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525614.0, "source_answer": 525614.0}
{"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 = {46: [(18, 15.0)], 398: [(18, 15.0)], 18: [(397, 0.17)], 397: [(521, 0.08)], 521: [(3, 2.0)], 3: [(940, 20.0)], 940: [(53, 0.0)], 53: [], 581: [(46, 0.33), (398, 0.33)]}\nsource = 581\ntarget = 53\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.58, "source_answer": 37.58}
{"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 = {896: [(43, 600.0)], 196: [(43, 600.0)], 43: [(500, 129600.0)], 500: [(179, 525600.0)], 179: [(622, 129600.0)], 622: [(220, 525600.0)], 220: [(936, 0.0)], 936: [], 381: [(896, 1200.0), (196, 300.0)]}\nsource = 381\ntarget = 936\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1312200.0, "source_answer": 1312200.0}
{"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 = {873: [(431, 15.0), (722, 10.0)], 431: [(304, 1.0)], 722: [(304, 1.0)], 304: [(820, 3.0)], 820: [(797, 0.0)], 797: [], 956: [(873, 15.0)]}\nsource = 956\ntarget = 797\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34.0, "source_answer": 34.0}
{"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 = {913: [(586, 0.25)], 586: [(751, 15.0)], 751: [(420, 15.0), (61, 15.0)], 420: [(590, 0.0)], 61: [(590, 0.0)], 590: [], 775: [(913, 2.0)]}\nsource = 775\ntarget = 590\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.25, "source_answer": 32.25}
{"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 = {290: [(308, 2.0)], 308: [(92, 3.0)], 92: [(56, 5.0)], 56: [(675, 2.0)], 888: [(979, 2.0)], 979: [(56, 5.0)], 675: [(908, 0.0)], 908: [], 376: [(290, 1.0), (888, 5.0)]}\nsource = 376\ntarget = 908\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": 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 = {651: [(304, 2.0)], 304: [(521, 10.0), (591, 10.0)], 591: [(115, 5.0)], 521: [(115, 5.0)], 115: [(989, 5.0)], 989: [(602, 1.0)], 602: [(600, 0.0)], 600: [], 293: [(651, 20.0)]}\nsource = 293\ntarget = 600\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": 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 = {194: [(801, 30.0)], 479: [(176, 120.0)], 176: [(717, 0.0)], 669: [(801, 30.0)], 801: [(479, 60.0)], 717: [], 147: [(194, 30.0), (669, 30.0)]}\nsource = 147\ntarget = 717\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": 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 = {353: [(872, 15.0)], 20: [(681, 2.0)], 681: [(980, 0.0)], 872: [(20, 10.0), (900, 1.0)], 900: [(681, 2.0)], 980: [], 728: [(353, 1.0)]}\nsource = 728\ntarget = 980\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": 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: [(917, 7200.0)], 917: [(328, 7200.0), (391, 7200.0)], 391: [(196, 7200.0)], 328: [(196, 7200.0)], 196: [(143, 7200.0)], 143: [(574, 0.0)], 574: [], 895: [(505, 7200.0)]}\nsource = 895\ntarget = 574\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36000.0, "source_answer": 36000.0}
{"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 = {664: [(486, 0.0)], 399: [(925, 15.0)], 925: [(293, 15.0)], 293: [(475, 10.0)], 475: [(486, 0.0)], 486: [], 160: [(664, 10.0), (399, 15.0)]}\nsource = 160\ntarget = 486\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": 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 = {461: [(840, 360.0)], 524: [(840, 360.0)], 840: [(816, 1.0)], 816: [(928, 5.0)], 928: [(929, 0.0)], 929: [], 70: [(461, 1440.0), (524, 60.0)]}\nsource = 70\ntarget = 929\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1806.0, "source_answer": 1806.0}
{"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 = {53: [(909, 3.0)], 57: [(817, 1.0)], 817: [(909, 3.0)], 909: [(367, 1.0)], 367: [(268, 0.5)], 268: [(904, 0.5)], 904: [(63, 0.0)], 63: [], 834: [(53, 10.0), (57, 0.17)]}\nsource = 834\ntarget = 63\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": 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 = {793: [(5, 10.0), (114, 20.0), (257, 10.0)], 5: [(754, 0.0)], 257: [(877, 10.0)], 114: [(877, 10.0)], 877: [(754, 0.0)], 754: [], 209: [(793, 10.0)]}\nsource = 209\ntarget = 754\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": 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 = {261: [(709, 0.5)], 519: [(709, 0.5)], 709: [(774, 0.5)], 774: [(202, 5.0)], 202: [(596, 5.0)], 596: [(410, 0.0)], 410: [], 452: [(261, 0.25), (519, 0.25)]}\nsource = 452\ntarget = 410\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.25, "source_answer": 11.25}
{"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 = {932: [(532, 0.75)], 733: [(41, 0.25), (459, 0.25)], 459: [(874, 0.0)], 41: [(874, 0.0)], 281: [(330, 0.58)], 330: [(874, 0.0)], 532: [(874, 0.0)], 874: [], 880: [(932, 0.42), (733, 0.42), (281, 0.42)]}\nsource = 880\ntarget = 874\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": 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 = {54: [(467, 2.0)], 467: [(855, 120.0), (349, 5.0)], 349: [(937, 259200.0)], 855: [(937, 259200.0)], 937: [(882, 120.0)], 882: [(711, 0.0)], 711: [], 97: [(54, 30.0)]}\nsource = 97\ntarget = 711\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 259472.0, "source_answer": 259472.0}
{"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 = {261: [(160, 5.0), (820, 5.0)], 160: [(514, 10.0)], 820: [(514, 10.0)], 514: [(7, 240.0)], 7: [(954, 0.0)], 954: [], 767: [(261, 2.0)]}\nsource = 767\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 257.0, "source_answer": 257.0}
{"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 = {406: [(966, 240.0)], 966: [(84, 60.0), (66, 60.0)], 66: [(884, 60.0)], 84: [(884, 60.0)], 884: [(817, 0.0)], 817: [], 150: [(406, 240.0)]}\nsource = 150\ntarget = 817\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": 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 = {819: [(368, 0.75)], 435: [(636, 0.58)], 636: [(638, 0.58)], 638: [(518, 0.33)], 518: [(879, 0.0)], 154: [(638, 0.58)], 368: [(518, 0.33)], 879: [], 255: [(819, 1.0), (435, 0.58), (154, 0.58)]}\nsource = 255\ntarget = 879\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.08, "source_answer": 2.08}
{"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 = {739: [(828, 3.0)], 962: [(80, 5.0)], 80: [(636, 3.0)], 636: [(828, 3.0)], 828: [(208, 1.0)], 208: [(56, 0.0)], 56: [], 697: [(739, 45.0), (962, 2.0)]}\nsource = 697\ntarget = 56\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": 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 = {502: [(871, 1.0), (312, 10.0), (176, 1.0)], 871: [(290, 1.0)], 176: [(480, 5.0)], 312: [(230, 40.0)], 290: [(230, 40.0)], 480: [(290, 1.0)], 230: [(383, 0.0)], 383: [], 403: [(502, 20.0)]}\nsource = 403\ntarget = 383\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": 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 = {720: [(819, 2.0), (55, 2.0)], 819: [(516, 2.0)], 55: [(516, 2.0)], 516: [(955, 2.0)], 955: [(196, 0.0)], 196: [], 610: [(720, 2.0)]}\nsource = 610\ntarget = 196\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": 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 = {708: [(670, 0.08), (124, 0.17)], 670: [(347, 0.33)], 124: [(347, 0.33)], 347: [(891, 0.25)], 891: [(693, 25.0)], 693: [(493, 0.0)], 493: [], 123: [(708, 0.05)]}\nsource = 123\ntarget = 493\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.8, "source_answer": 25.8}
{"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 = {823: [(556, 2.0)], 556: [(784, 0.58), (618, 2.0)], 618: [(280, 3.0)], 784: [(87, 2.0)], 280: [(87, 2.0)], 87: [(940, 35.0)], 940: [(90, 0.0)], 90: [], 906: [(823, 6832800.0)]}\nsource = 906\ntarget = 90\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6832844.0, "source_answer": 6832844.0}
{"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 = {901: [(632, 1.0)], 632: [(255, 2.0)], 255: [(301, 15.0), (343, 5.0)], 301: [(341, 10.0)], 343: [(341, 10.0)], 341: [(748, 60.0)], 748: [(553, 0.0)], 553: [], 233: [(901, 2.0)]}\nsource = 233\ntarget = 553\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": 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 = {489: [(999, 1440.0), (103, 1440.0)], 999: [(885, 180.0)], 103: [(885, 180.0)], 885: [(682, 1440.0)], 682: [(14, 240.0)], 14: [(15, 0.0)], 15: [], 749: [(489, 60.0)]}\nsource = 749\ntarget = 15\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3360.0, "source_answer": 3360.0}
{"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 = {581: [(957, 0.5)], 957: [(443, 30.0)], 443: [(603, 20.0)], 603: [(65, 10.0)], 65: [(322, 900.0), (974, 900.0)], 322: [(798, 0.0)], 974: [(798, 0.0)], 798: [], 502: [(581, 0.08)]}\nsource = 502\ntarget = 798\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 960.58, "source_answer": 960.58}
{"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 = {952: [(716, 60.0)], 716: [(747, 10.0)], 747: [(355, 60.0), (496, 10.0)], 355: [(374, 5.0)], 496: [(374, 5.0)], 374: [(665, 0.0)], 665: [], 569: [(952, 60.0)]}\nsource = 569\ntarget = 665\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": 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 = {324: [(384, 1.0)], 384: [(874, 60.0), (834, 60.0)], 834: [(503, 525600.0)], 874: [(503, 525600.0)], 503: [(744, 0.0)], 744: [], 333: [(324, 1.0)]}\nsource = 333\ntarget = 744\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525662.0, "source_answer": 525662.0}
{"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 = {590: [(325, 1.0)], 632: [(325, 1.0)], 325: [(451, 15.0)], 451: [(237, 10.0)], 237: [(913, 0.0)], 913: [], 341: [(590, 2.0), (632, 1.0)]}\nsource = 341\ntarget = 913\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": 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 = {674: [(972, 0.5), (916, 0.5)], 972: [(181, 1.0)], 916: [(181, 1.0)], 181: [(877, 0.33)], 877: [(703, 1.0)], 703: [(243, 0.0)], 243: [], 430: [(674, 0.33)]}\nsource = 430\ntarget = 243\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": 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 = {639: [(621, 5.0)], 339: [(621, 5.0)], 621: [(833, 0.0)], 833: [], 811: [(639, 30.0), (339, 10.0)]}\nsource = 811\ntarget = 833\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": 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 = {345: [(70, 1.0)], 70: [(460, 0.0)], 934: [(460, 0.0)], 460: [], 107: [(345, 5.0), (934, 3.0)]}\nsource = 107\ntarget = 460\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": 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 = {139: [(868, 1440.0)], 21: [(868, 1440.0)], 868: [(678, 0.0)], 678: [], 279: [(139, 1440.0), (21, 1440.0)]}\nsource = 279\ntarget = 678\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2880.0, "source_answer": 2880.0}
{"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 = {291: [(331, 10.0)], 331: [(655, 0.0)], 740: [(655, 0.0)], 655: [], 720: [(291, 30.0), (740, 20.0)]}\nsource = 720\ntarget = 655\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": 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 = {555: [(816, 20.0)], 631: [(816, 20.0)], 816: [(528, 0.0)], 528: [], 648: [(555, 10.0), (631, 15.0)]}\nsource = 648\ntarget = 528\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": 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 = {57: [(610, 0.0)], 117: [(610, 0.0)], 864: [(117, 10.0), (57, 10.0)], 610: [], 598: [(864, 20.0)]}\nsource = 598\ntarget = 610\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": 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 = {881: [(306, 4320.0)], 726: [(306, 4320.0)], 306: [(644, 0.0)], 644: [], 878: [(881, 30.0), (726, 60.0)]}\nsource = 878\ntarget = 644\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4380.0, "source_answer": 4380.0}
{"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 = {388: [(806, 0.0)], 245: [(388, 5.0)], 73: [(388, 5.0)], 806: [], 242: [(245, 10.0), (73, 15.0)]}\nsource = 242\ntarget = 806\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": 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 = {168: [(610, 0.02)], 702: [(610, 0.02)], 408: [(610, 0.02)], 610: [(172, 0.0)], 172: [], 880: [(168, 0.03), (702, 0.02), (408, 0.02)]}\nsource = 880\ntarget = 172\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.05, "source_answer": 0.05}
{"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 = {523: [(958, 0.0)], 214: [(967, 10080.0)], 967: [(958, 0.0)], 958: [], 446: [(523, 10.0), (214, 30.0)]}\nsource = 446\ntarget = 958\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": 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 = {696: [(772, 1.0)], 772: [(931, 1.0)], 931: [(548, 0.0)], 180: [(548, 0.0)], 548: [], 132: [(696, 1.0), (180, 1.0)]}\nsource = 132\ntarget = 548\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": 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 = {229: [(266, 5.0), (580, 3.0)], 266: [(812, 0.0)], 580: [(812, 0.0)], 812: [], 79: [(229, 2.0)]}\nsource = 79\ntarget = 812\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": 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 = {599: [(925, 5.0)], 19: [(925, 5.0)], 480: [(925, 5.0)], 925: [(890, 0.0)], 890: [], 515: [(599, 5.0), (19, 2.0), (480, 2.0)]}\nsource = 515\ntarget = 890\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": 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 = {845: [(51, 10.0)], 51: [(916, 5.0)], 916: [(831, 0.0)], 355: [(831, 0.0)], 831: [], 969: [(845, 5.0), (355, 30.0)]}\nsource = 969\ntarget = 831\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": 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 = {12: [(910, 3.0)], 128: [(910, 3.0)], 20: [(910, 3.0)], 910: [(871, 0.0)], 871: [], 253: [(12, 5.0), (128, 3.0), (20, 10.0)]}\nsource = 253\ntarget = 871\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": 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 = {341: [(336, 10.0)], 336: [(283, 15.0)], 283: [(844, 0.0)], 745: [(844, 0.0)], 844: [], 674: [(341, 5.0), (745, 10.0)]}\nsource = 674\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": 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 = {858: [(837, 120.0)], 837: [(361, 0.0)], 153: [(361, 0.0)], 361: [], 7: [(858, 60.0), (153, 300.0)]}\nsource = 7\ntarget = 361\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": 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 = {93: [(951, 30.0), (887, 15.0), (138, 20.0)], 887: [(681, 0.0)], 138: [(681, 0.0)], 951: [(681, 0.0)], 681: [], 220: [(93, 10.0)]}\nsource = 220\ntarget = 681\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": 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 = {655: [(513, 20160.0)], 448: [(513, 20160.0)], 513: [(148, 2880.0)], 148: [(295, 0.0)], 295: [], 889: [(655, 1440.0), (448, 2880.0)]}\nsource = 889\ntarget = 295\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": 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 = {417: [(93, 20.0)], 963: [(332, 0.0)], 93: [(332, 0.0)], 538: [(93, 20.0)], 332: [], 856: [(417, 10.0), (963, 15.0), (538, 30.0)]}\nsource = 856\ntarget = 332\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": 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 = {564: [(380, 60.0), (479, 120.0), (494, 60.0)], 479: [(443, 0.0)], 494: [(443, 0.0)], 380: [(443, 0.0)], 443: [], 233: [(564, 60.0)]}\nsource = 233\ntarget = 443\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": 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 = {266: [(918, 1.0)], 918: [(287, 0.0)], 487: [(102, 1.0)], 102: [(287, 0.0)], 287: [], 479: [(266, 1.0), (487, 2.0)]}\nsource = 479\ntarget = 287\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": 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 = {640: [(577, 0.0)], 267: [(640, 10.0)], 750: [(577, 0.0)], 39: [(577, 0.0)], 577: [], 620: [(267, 10.0), (750, 15.0), (39, 5.0)]}\nsource = 620\ntarget = 577\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": 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 = {248: [(123, 0.0)], 942: [(248, 30.0)], 271: [(248, 30.0)], 123: [], 86: [(942, 10080.0), (271, 5.0)]}\nsource = 86\ntarget = 123\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": 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 = {614: [(424, 20.0)], 267: [(378, 5.0)], 424: [(378, 5.0)], 378: [(23, 0.0)], 23: [], 828: [(614, 15.0), (267, 5.0)]}\nsource = 828\ntarget = 23\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": 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 = {854: [(771, 15.0)], 362: [(771, 15.0)], 692: [(465, 2.0)], 465: [(771, 15.0)], 771: [(681, 0.0)], 681: [], 380: [(854, 5.0), (362, 10.0), (692, 5.0)]}\nsource = 380\ntarget = 681\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": 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 = {887: [(527, 1.0)], 728: [(527, 1.0)], 106: [(527, 1.0)], 527: [(363, 1.0)], 363: [(672, 0.0)], 672: [], 506: [(887, 5.0), (728, 2.0), (106, 1.0)]}\nsource = 506\ntarget = 672\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": 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 = {880: [(492, 0.0)], 283: [(463, 20.0)], 463: [(492, 0.0)], 492: [], 576: [(880, 20160.0), (283, 40320.0)]}\nsource = 576\ntarget = 492\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40340.0, "source_answer": 40340.0}
{"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 = {144: [(974, 4.0)], 628: [(166, 2.0)], 166: [(45, 1.0)], 45: [(974, 4.0)], 974: [(999, 4.0)], 999: [(300, 3.0)], 300: [(759, 0.0)], 759: [], 682: [(144, 10.0), (628, 1.0)]}\nsource = 682\ntarget = 759\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": 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 = {391: [(333, 3.0)], 901: [(333, 3.0)], 883: [(333, 3.0)], 333: [(538, 0.0)], 538: [], 957: [(391, 5.0), (901, 2.0), (883, 10.0)]}\nsource = 957\ntarget = 538\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": 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 = {46: [(107, 15.0), (470, 10.0)], 107: [(500, 0.0)], 470: [(500, 0.0)], 500: [], 933: [(46, 30.0)]}\nsource = 933\ntarget = 500\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": 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 = {114: [(526, 10.0), (404, 10.0), (769, 15.0)], 526: [(609, 0.0)], 769: [(609, 0.0)], 404: [(609, 0.0)], 521: [(609, 0.0)], 476: [(54, 15.0), (521, 5.0)], 54: [(609, 0.0)], 609: [], 272: [(114, 5.0), (476, 10.0)]}\nsource = 272\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": 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 = {942: [(884, 0.0)], 830: [(253, 15.0)], 957: [(884, 0.0)], 25: [(884, 0.0)], 445: [(884, 0.0)], 253: [(884, 0.0)], 485: [(884, 0.0)], 884: [], 58: [(942, 30.0), (830, 20.0), (957, 60.0), (25, 45.0), (445, 25.0), (485, 60.0)]}\nsource = 58\ntarget = 884\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": 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 = {940: [(460, 5.0)], 972: [(460, 5.0)], 296: [(460, 5.0)], 192: [(460, 5.0)], 377: [(460, 5.0)], 460: [(771, 1.0)], 771: [(768, 0.0)], 768: [(617, 0.0)], 617: [], 658: [(940, 10.0), (972, 2.0), (296, 2.0), (192, 5.0), (377, 3.0)]}\nsource = 658\ntarget = 617\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": 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 = {242: [(565, 0.0)], 928: [(242, 30.0)], 221: [(242, 30.0)], 565: [], 967: [(928, 15.0), (221, 20.0)]}\nsource = 967\ntarget = 565\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": 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 = {342: [(132, 2.0)], 132: [(211, 10.0)], 211: [(172, 20.0)], 172: [(667, 2.0)], 816: [(398, 5.0)], 667: [(398, 5.0)], 398: [(995, 1.0)], 995: [(110, 0.0)], 110: [], 303: [(342, 10.0), (816, 5.0)]}\nsource = 303\ntarget = 110\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": 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 = {805: [(448, 5.0)], 426: [(790, 1.0)], 41: [(448, 5.0)], 448: [(790, 1.0)], 790: [(843, 4320.0)], 843: [(189, 60.0)], 189: [(624, 0.0)], 624: [], 16: [(805, 5.0), (426, 2.0), (41, 1.0)]}\nsource = 16\ntarget = 624\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4391.0, "source_answer": 4391.0}
{"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 = {925: [(46, 10.0)], 46: [(371, 15.0)], 371: [(350, 2.0)], 350: [(901, 5.0), (475, 5.0)], 475: [(138, 20.0)], 138: [(161, 30.0)], 161: [(187, 0.0)], 901: [(187, 0.0)], 187: [], 366: [(925, 5.0)]}\nsource = 366\ntarget = 187\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 87.0, "source_answer": 87.0}
{"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 = {918: [(173, 5.0)], 388: [(130, 5.0)], 130: [(250, 10.0)], 819: [(250, 10.0)], 250: [(173, 5.0)], 173: [(14, 15.0)], 14: [(479, 0.0)], 479: [], 663: [(918, 5.0), (388, 10.0), (819, 5.0)]}\nsource = 663\ntarget = 479\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": 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 = {337: [(524, 3.0)], 1: [(337, 2.0)], 524: [(303, 5.0), (583, 2.0), (651, 15.0), (692, 10.0), (792, 7.0)], 863: [(303, 5.0), (583, 2.0), (692, 10.0), (792, 7.0), (651, 15.0)], 692: [(512, 0.0)], 303: [(512, 0.0)], 792: [(512, 0.0)], 583: [(512, 0.0)], 651: [(512, 0.0)], 512: [], 829: [(1, 5.0), (863, 5.0)]}\nsource = 829\ntarget = 512\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": 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 = {680: [(216, 5.0)], 609: [(216, 5.0)], 487: [(216, 5.0)], 751: [(216, 5.0)], 219: [(216, 5.0)], 38: [(216, 5.0)], 356: [(216, 5.0)], 542: [(216, 5.0)], 216: [(231, 0.0)], 231: [], 385: [(680, 5.0), (609, 10.0), (487, 15.0), (751, 5.0), (219, 10.0), (38, 20.0), (356, 5.0), (542, 15.0)]}\nsource = 385\ntarget = 231\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": 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 = {531: [(796, 40.0)], 224: [(125, 2.0)], 386: [(125, 2.0)], 125: [(796, 40.0)], 796: [(684, 0.0)], 684: [], 475: [(531, 5.0), (224, 2.0), (386, 5.0)]}\nsource = 475\ntarget = 684\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": 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 = {940: [(281, 5.0)], 843: [(633, 3.0)], 281: [(843, 2.0)], 833: [(633, 3.0)], 663: [(633, 3.0)], 632: [(633, 3.0)], 383: [(633, 3.0)], 633: [(945, 1.0)], 945: [(461, 0.0)], 461: [], 865: [(940, 2.0), (833, 2.0), (663, 1.0), (632, 2.0), (383, 2.0)]}\nsource = 865\ntarget = 461\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": 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 = {96: [(255, 15.0)], 359: [(255, 15.0)], 255: [(722, 120.0)], 722: [(391, 0.0)], 391: [], 630: [(96, 15.0), (359, 60.0)]}\nsource = 630\ntarget = 391\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": 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 = {684: [(571, 2880.0)], 2: [(956, 0.0)], 414: [(571, 2880.0)], 3: [(571, 2880.0)], 571: [(758, 1440.0), (840, 1440.0)], 840: [(416, 20160.0)], 758: [(173, 2880.0)], 173: [(416, 20160.0)], 416: [(761, 120.0)], 761: [(2, 60480.0)], 956: [], 403: [(684, 2880.0), (414, 4320.0), (3, 2880.0)]}\nsource = 403\ntarget = 956\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 92280.0, "source_answer": 92280.0}
{"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 = {934: [(502, 2.0)], 458: [(250, 0.0)], 502: [(463, 5.0)], 920: [(195, 5.0)], 195: [(493, 5.0)], 493: [(484, 7.0)], 455: [(412, 8.0)], 463: [(920, 3.0)], 484: [(455, 6.0)], 412: [(250, 0.0)], 250: [], 689: [(934, 5.0), (458, 2.0)]}\nsource = 689\ntarget = 250\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46.0, "source_answer": 46.0}
{"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 = {978: [(837, 0.03)], 478: [(837, 0.03)], 837: [(9, 0.0)], 689: [(837, 0.03)], 470: [(837, 0.03)], 890: [(837, 0.03)], 857: [(837, 0.03)], 492: [(837, 0.03)], 830: [(837, 0.03)], 918: [(837, 0.03)], 192: [(837, 0.03)], 9: [], 566: [(978, 0.02), (689, 0.02), (470, 0.02), (890, 0.02), (857, 0.02), (492, 0.02), (830, 0.02), (918, 0.02), (192, 0.02), (478, 0.02)]}\nsource = 566\ntarget = 9\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.05, "source_answer": 0.05}
{"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 = {880: [(334, 5.0)], 273: [(334, 5.0)], 551: [(84, 5.0)], 334: [(84, 5.0)], 84: [(738, 5.0)], 738: [(345, 2.0)], 345: [(372, 0.0)], 372: [], 123: [(880, 5.0), (273, 10.0), (551, 10.0)]}\nsource = 123\ntarget = 372\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": 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 = {958: [(36, 5.0)], 226: [(968, 20.0)], 36: [(133, 3.0)], 133: [(968, 20.0)], 968: [(626, 0.0)], 626: [(889, 0.0)], 889: [], 485: [(958, 15.0), (226, 15.0)]}\nsource = 485\ntarget = 889\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": 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 = {367: [(840, 10.0)], 203: [(52, 10.0)], 755: [(52, 10.0)], 52: [(527, 0.0)], 840: [(526, 5.0), (687, 10.0), (203, 5.0), (255, 10.0), (610, 10.0), (867, 5.0), (406, 10.0), (755, 10.0), (631, 5.0)], 406: [(52, 10.0)], 867: [(52, 10.0)], 255: [(52, 10.0)], 631: [(52, 10.0)], 526: [(52, 10.0)], 610: [(52, 10.0)], 687: [(52, 10.0)], 527: [], 344: [(367, 30.0)]}\nsource = 344\ntarget = 527\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": 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 = {328: [(195, 300.0), (783, 60.0), (667, 120.0), (110, 300.0)], 129: [(449, 0.0)], 279: [(449, 0.0)], 326: [(449, 0.0)], 561: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 638: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 88: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 783: [(279, 120.0), (326, 120.0), (129, 60.0), (179, 30.0)], 195: [(279, 120.0), (129, 60.0), (326, 120.0), (179, 30.0)], 110: [(129, 60.0), (326, 120.0), (179, 30.0), (279, 120.0)], 667: [(279, 120.0), (129, 60.0), (326, 120.0), (179, 30.0)], 179: [(449, 0.0)], 449: [], 498: [(328, 30.0), (561, 45.0), (638, 15.0), (88, 60.0)]}\nsource = 498\ntarget = 449\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": 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 = {542: [(834, 5.0)], 952: [(888, 1.0)], 888: [(181, 1.0)], 181: [(913, 1.0)], 913: [(87, 0.0)], 44: [(542, 5.0)], 647: [(542, 5.0)], 894: [(542, 5.0)], 834: [(419, 15.0)], 419: [(597, 3.0)], 597: [(961, 2.0)], 961: [(748, 2.0)], 748: [(952, 120.0)], 87: [], 394: [(44, 2.0), (647, 2.0), (894, 1.0)]}\nsource = 394\ntarget = 87\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 157.0, "source_answer": 157.0}
{"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 = {371: [(946, 30.0)], 651: [(555, 2.0)], 555: [(929, 7.0)], 929: [(567, 1.0)], 567: [(764, 0.0)], 764: [(950, 0.0)], 946: [(923, 5.0)], 923: [(879, 2.0), (435, 1.0)], 879: [(63, 2.0)], 435: [(374, 1.0), (963, 5.0)], 374: [(63, 2.0)], 63: [(811, 2.0)], 963: [(811, 2.0)], 811: [(651, 10.0)], 950: [], 982: [(371, 5.0)]}\nsource = 982\ntarget = 950\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": 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 = {751: [(757, 20160.0), (447, 1440.0), (954, 20160.0), (773, 40320.0)], 954: [(408, 0.0)], 773: [(408, 0.0)], 757: [(408, 0.0)], 447: [(408, 0.0)], 408: [], 784: [(751, 1440.0)]}\nsource = 784\ntarget = 408\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": 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 = {938: [(65, 0.0)], 549: [(434, 10.0), (246, 15.0)], 246: [(778, 5.0)], 434: [(778, 5.0)], 778: [(65, 0.0)], 65: [], 387: [(938, 5.0), (549, 30.0)]}\nsource = 387\ntarget = 65\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": 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 = {574: [(735, 0.0)], 791: [(705, 30.0)], 705: [(735, 0.0)], 735: [], 361: [(574, 60.0), (791, 120.0)]}\nsource = 361\ntarget = 735\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": 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 = {636: [(205, 5.0)], 691: [(205, 5.0)], 205: [(331, 0.0)], 331: [], 910: [(636, 10.0), (691, 2.0)]}\nsource = 910\ntarget = 331\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": 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 = {157: [(550, 15.0)], 550: [(842, 0.0)], 978: [(842, 0.0)], 418: [(842, 0.0)], 432: [(842, 0.0)], 842: [], 741: [(157, 30.0), (978, 60.0), (418, 60.0), (432, 120.0)]}\nsource = 741\ntarget = 842\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": 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 = {786: [(749, 30.0)], 847: [(749, 30.0)], 749: [(787, 45.0)], 787: [(78, 10.0)], 78: [(205, 0.0)], 205: [], 496: [(786, 20.0), (847, 20.0)]}\nsource = 496\ntarget = 205\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": 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 = {306: [(588, 30.0)], 829: [(588, 30.0)], 568: [(588, 30.0)], 652: [(356, 0.0)], 588: [(652, 480.0)], 356: [], 782: [(306, 10.0), (829, 20.0), (568, 30.0)]}\nsource = 782\ntarget = 356\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": 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 = {181: [(156, 0.0)], 790: [(640, 1.0)], 640: [(156, 0.0)], 156: [], 413: [(181, 2.0), (790, 5.0)]}\nsource = 413\ntarget = 156\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": 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 = {976: [(442, 10.0)], 56: [(70, 0.0)], 727: [(442, 10.0)], 442: [(56, 1440.0)], 70: [], 207: [(976, 15.0), (727, 5.0)]}\nsource = 207\ntarget = 70\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": 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 = {385: [(898, 0.0)], 766: [(898, 0.0)], 17: [(898, 0.0)], 820: [(390, 60.0), (385, 30.0), (766, 45.0), (17, 15.0)], 390: [(898, 0.0)], 898: [], 118: [(820, 60.0)]}\nsource = 118\ntarget = 898\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": 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 = {593: [(977, 2.0)], 977: [(869, 3.0)], 869: [(996, 0.0)], 691: [(977, 2.0)], 996: [], 811: [(593, 5.0), (691, 1.0)]}\nsource = 811\ntarget = 996\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": 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 = {333: [(375, 0.0)], 329: [(333, 30.0)], 336: [(375, 0.0)], 375: [], 514: [(329, 15.0), (336, 15.0)]}\nsource = 514\ntarget = 375\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": 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 = {788: [(673, 5.0), (817, 10.0), (602, 10.0), (79, 5.0), (56, 5.0)], 602: [(374, 0.0)], 79: [(374, 0.0)], 673: [(374, 0.0)], 56: [(374, 0.0)], 817: [(374, 0.0)], 374: [], 636: [(788, 5.0)]}\nsource = 636\ntarget = 374\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": 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 = {89: [(783, 5.0)], 150: [(783, 5.0)], 627: [(783, 5.0)], 901: [(783, 5.0)], 588: [(783, 5.0)], 783: [(640, 0.0)], 640: [], 86: [(89, 15.0), (150, 10.0), (627, 10.0), (901, 10.0), (588, 10.0)]}\nsource = 86\ntarget = 640\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": 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 = {343: [(363, 20.0), (253, 10.0), (831, 15.0)], 253: [(323, 5.0)], 831: [(323, 5.0)], 363: [(323, 5.0)], 323: [(695, 10.0)], 695: [(258, 0.0)], 258: [], 13: [(343, 5.0)]}\nsource = 13\ntarget = 258\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": 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 = {230: [(556, 1.0)], 556: [(822, 0.5)], 822: [(777, 1.0)], 777: [(936, 15.0)], 936: [(749, 0.0)], 12: [(749, 0.0)], 749: [], 941: [(230, 5.0), (12, 10.0)]}\nsource = 941\ntarget = 749\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": 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 = {862: [(472, 0.0)], 343: [(472, 0.0)], 479: [(51, 2.0)], 51: [(472, 0.0)], 472: [], 958: [(862, 2.0), (343, 3.0), (479, 1.0)]}\nsource = 958\ntarget = 472\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": 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 = {876: [(778, 2880.0)], 142: [(621, 4.0)], 621: [(157, 60.0)], 157: [(30, 0.0)], 972: [(181, 10080.0)], 181: [(157, 60.0)], 778: [(157, 60.0)], 30: [], 830: [(876, 60.0), (142, 2.0), (972, 10.0)]}\nsource = 830\ntarget = 30\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10150.0, "source_answer": 10150.0}
{"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 = {245: [(702, 129600.0)], 751: [(702, 129600.0)], 702: [(81, 10.0)], 81: [(309, 60.0)], 503: [(702, 129600.0)], 309: [(851, 0.0)], 851: [], 601: [(245, 1440.0), (751, 30.0), (503, 4320.0)]}\nsource = 601\ntarget = 851\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 133990.0, "source_answer": 133990.0}
{"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 = {206: [(634, 0.0)], 827: [(9, 5.0)], 9: [(634, 0.0)], 483: [(969, 30.0)], 969: [(634, 0.0)], 201: [(457, 5.0)], 457: [(634, 0.0)], 634: [], 406: [(206, 2.0), (827, 15.0), (483, 15.0), (201, 10.0)]}\nsource = 406\ntarget = 634\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": 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 = {121: [(66, 30.0)], 10: [(920, 0.0)], 66: [(570, 30.0)], 570: [(54, 30.0)], 54: [(362, 30.0), (10, 5.0)], 362: [(920, 0.0)], 920: [], 217: [(121, 30.0)]}\nsource = 217\ntarget = 920\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": 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 = {13: [(899, 10.0), (80, 10.0)], 899: [(35, 10.0)], 80: [(35, 10.0)], 35: [(624, 10.0)], 624: [(795, 10.0)], 795: [(441, 10.0)], 441: [(308, 0.0)], 308: [], 57: [(13, 10.0)]}\nsource = 57\ntarget = 308\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": 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 = {394: [(47, 60.0)], 47: [(135, 60480.0), (83, 60.0)], 83: [(790, 60.0)], 135: [(790, 60.0)], 790: [(301, 0.0)], 301: [], 951: [(394, 0.5)]}\nsource = 951\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60600.5, "source_answer": 60600.5}
{"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 = {900: [(166, 0.25)], 217: [(166, 0.25)], 166: [(269, 5.0)], 269: [(333, 15.0)], 333: [(275, 5.0)], 275: [(636, 0.0)], 636: [], 396: [(900, 30.0), (217, 30.0)]}\nsource = 396\ntarget = 636\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.25, "source_answer": 55.25}
{"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 = {795: [(798, 1.0)], 798: [(925, 1.0)], 925: [(251, 1.0), (945, 1.0)], 251: [(274, 1.0)], 945: [(274, 1.0)], 274: [(591, 1.0)], 591: [(104, 0.0)], 104: [], 773: [(795, 1.0)]}\nsource = 773\ntarget = 104\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": 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 = {974: [(224, 30.0)], 814: [(224, 30.0)], 224: [(808, 10.0)], 808: [(136, 30.0)], 136: [(228, 10.0)], 228: [(654, 0.0)], 654: [], 595: [(974, 2.0), (814, 10.0)]}\nsource = 595\ntarget = 654\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": 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 = {415: [(320, 120.0)], 416: [(320, 120.0)], 320: [(273, 1440.0)], 273: [(96, 172800.0)], 96: [(800, 10080.0), (936, 10080.0)], 800: [(898, 0.0)], 936: [(898, 0.0)], 898: [], 141: [(415, 60.0), (416, 120.0)]}\nsource = 141\ntarget = 898\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 184560.0, "source_answer": 184560.0}
{"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 = {346: [(762, 1.0)], 762: [(180, 0.25)], 180: [(207, 1.0)], 207: [(27, 0.17), (41, 0.17), (277, 0.17)], 41: [(696, 0.0)], 27: [(696, 0.0)], 277: [(696, 0.0)], 696: [], 102: [(346, 0.25)]}\nsource = 102\ntarget = 696\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": 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 = {658: [(778, 15.0)], 827: [(778, 15.0)], 778: [(260, 1.0)], 260: [(528, 1.0)], 104: [(778, 15.0)], 528: [(980, 1.0)], 980: [(167, 0.0)], 167: [], 165: [(658, 10.0), (827, 1.0), (104, 15.0)]}\nsource = 165\ntarget = 167\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": 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 = {8: [(329, 0.5), (288, 180.0)], 329: [(653, 0.17)], 288: [(653, 0.17)], 653: [(512, 0.08)], 512: [(639, 129600.0)], 639: [(894, 0.0)], 894: [], 120: [(8, 60.0)]}\nsource = 120\ntarget = 894\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129840.25, "source_answer": 129840.25}
{"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 = {425: [(264, 120.0), (520, 120.0)], 264: [(229, 120.0)], 520: [(229, 120.0)], 229: [(917, 10.0)], 917: [(643, 0.0)], 643: [], 711: [(425, 60.0)]}\nsource = 711\ntarget = 643\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 310.0, "source_answer": 310.0}
{"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 = {776: [(82, 10.0), (480, 10.0)], 82: [(362, 45.0)], 480: [(362, 45.0)], 362: [(789, 5.0)], 789: [(244, 5.0)], 244: [(652, 0.0)], 652: [], 853: [(776, 5.0)]}\nsource = 853\ntarget = 652\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": 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 = {66: [(489, 5.0), (100, 10.0)], 489: [(889, 5.0)], 100: [(889, 5.0)], 889: [(333, 10.0)], 333: [(454, 10.0)], 454: [(694, 10.0)], 694: [(845, 0.0)], 845: [], 610: [(66, 10.0)]}\nsource = 610\ntarget = 845\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": 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 = {832: [(850, 30.0), (977, 60.0)], 850: [(500, 10.0)], 977: [(500, 10.0)], 500: [(778, 30.0)], 778: [(91, 0.0)], 91: [], 521: [(832, 30.0)]}\nsource = 521\ntarget = 91\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": 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 = {302: [(610, 1.0), (689, 180.0)], 689: [(835, 1440.0)], 610: [(345, 1440.0)], 835: [(949, 0.0)], 345: [(949, 0.0)], 949: [], 49: [(302, 0.25)]}\nsource = 49\ntarget = 949\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1620.25, "source_answer": 1620.25}
{"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 = {483: [(991, 10.0)], 469: [(991, 10.0)], 991: [(137, 3.0)], 137: [(795, 5.0)], 795: [(780, 0.0)], 780: [], 733: [(483, 0.5), (469, 0.5)]}\nsource = 733\ntarget = 780\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.5, "source_answer": 18.5}
{"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 = {952: [(916, 2.0)], 916: [(691, 3.0)], 691: [(276, 4.0)], 276: [(529, 3.0), (74, 3.0)], 74: [(11, 0.0)], 529: [(11, 0.0)], 11: [], 374: [(952, 3.0)]}\nsource = 374\ntarget = 11\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": 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 = {360: [(16, 0.0)], 844: [(932, 15.0)], 932: [(16, 0.0)], 16: [], 939: [(360, 5.0), (844, 10.0)]}\nsource = 939\ntarget = 16\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": 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 = {690: [(46, 20160.0)], 298: [(46, 20160.0)], 142: [(789, 0.0)], 46: [(142, 43200.0)], 789: [], 386: [(690, 86400.0), (298, 129600.0)]}\nsource = 386\ntarget = 789\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": 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 = {54: [(287, 0.0)], 706: [(54, 5.0)], 567: [(54, 5.0)], 287: [], 955: [(706, 10.0), (567, 15.0)]}\nsource = 955\ntarget = 287\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": 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 = {601: [(130, 0.0)], 889: [(130, 0.0)], 246: [(130, 0.0)], 940: [(889, 1440.0), (601, 10080.0), (246, 20160.0)], 130: [], 830: [(940, 43200.0)]}\nsource = 830\ntarget = 130\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": 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 = {297: [(694, 15.0)], 693: [(694, 15.0)], 694: [(233, 2.0)], 233: [(890, 0.0)], 890: [], 362: [(297, 15.0), (693, 10.0)]}\nsource = 362\ntarget = 890\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": 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 = {465: [(551, 10.0)], 551: [(447, 2.0)], 447: [(298, 0.0)], 841: [(298, 0.0)], 298: [], 821: [(465, 5.0), (841, 1.0)]}\nsource = 821\ntarget = 298\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": 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 = {393: [(709, 0.5)], 709: [(50, 2.0)], 50: [(31, 0.0)], 146: [(50, 2.0)], 31: [], 946: [(393, 1.0), (146, 1.0)]}\nsource = 946\ntarget = 31\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": 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 = {671: [(403, 0.0)], 505: [(16, 20160.0)], 16: [(403, 0.0)], 403: [], 586: [(671, 20160.0), (505, 40320.0)]}\nsource = 586\ntarget = 403\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60480.0, "source_answer": 60480.0}
{"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 = {376: [(433, 10.0)], 433: [(846, 240.0)], 846: [(683, 0.0)], 146: [(619, 240.0)], 619: [(433, 10.0)], 683: [], 757: [(376, 2.0), (146, 20.0)]}\nsource = 757\ntarget = 683\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 510.0, "source_answer": 510.0}
{"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 = {581: [(208, 360.0)], 259: [(208, 360.0)], 746: [(208, 360.0)], 208: [(122, 1.0)], 122: [(418, 0.0)], 418: [], 897: [(581, 5.0), (259, 10.0), (746, 2.0)]}\nsource = 897\ntarget = 418\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 371.0, "source_answer": 371.0}
{"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 = {438: [(733, 10.0)], 268: [(733, 10.0)], 733: [(499, 30.0)], 499: [(561, 5.0)], 561: [(915, 0.0)], 915: [], 164: [(438, 10.0), (268, 20.0)]}\nsource = 164\ntarget = 915\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": 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 = {72: [(960, 20.0), (146, 10.0), (667, 15.0), (875, 15.0)], 667: [(185, 0.0)], 875: [(185, 0.0)], 960: [(185, 0.0)], 146: [(185, 0.0)], 185: [], 701: [(72, 10.0)]}\nsource = 701\ntarget = 185\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": 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 = {713: [(854, 0.0)], 8: [(713, 5.0)], 997: [(854, 0.0)], 854: [], 727: [(8, 10.0), (997, 15.0)]}\nsource = 727\ntarget = 854\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": 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 = {245: [(266, 2.0)], 266: [(305, 7.0)], 656: [(305, 7.0)], 305: [(537, 0.0)], 517: [(537, 0.0)], 537: [], 176: [(245, 2.0), (656, 1.0), (517, 8.0)]}\nsource = 176\ntarget = 537\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": 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 = {416: [(948, 10.0)], 948: [(936, 0.0)], 53: [(936, 0.0)], 661: [(936, 0.0)], 628: [(936, 0.0)], 936: [], 205: [(416, 5.0), (53, 2.0), (661, 3.0), (628, 1.0)]}\nsource = 205\ntarget = 936\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": 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 = {507: [(152, 30.0), (71, 10.0)], 152: [(208, 5.0)], 71: [(208, 5.0)], 208: [(755, 60.0)], 755: [(734, 0.0)], 734: [], 231: [(507, 10.0)]}\nsource = 231\ntarget = 734\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": 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 = {368: [(77, 1440.0)], 77: [(510, 0.0)], 113: [(510, 0.0)], 510: [], 436: [(368, 10080.0), (113, 43200.0)]}\nsource = 436\ntarget = 510\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": 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 = {58: [(649, 3.0)], 649: [(552, 3.0)], 182: [(552, 3.0)], 552: [(809, 12.0)], 809: [(496, 0.0)], 496: [], 45: [(58, 5.0), (182, 2.0)]}\nsource = 45\ntarget = 496\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": 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 = {953: [(810, 5.0)], 496: [(967, 10.0), (810, 5.0)], 810: [(572, 0.0)], 967: [(502, 5.0)], 502: [(572, 0.0)], 572: [], 135: [(953, 5.0), (496, 10.0)]}\nsource = 135\ntarget = 572\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": 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 = {314: [(390, 3.0)], 113: [(390, 3.0)], 923: [(390, 3.0)], 390: [(718, 0.0)], 718: [], 416: [(314, 5.0), (113, 2.0), (923, 30.0)]}\nsource = 416\ntarget = 718\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": 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 = {366: [(396, 180.0)], 396: [(349, 0.0)], 494: [(349, 0.0)], 609: [(349, 0.0)], 676: [(349, 0.0)], 349: [], 703: [(366, 60.0), (494, 10.0), (609, 30.0), (676, 15.0)]}\nsource = 703\ntarget = 349\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": 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 = {611: [(184, 0.0)], 768: [(611, 2.0)], 845: [(611, 2.0)], 762: [(611, 2.0)], 700: [(184, 0.0)], 184: [], 17: [(768, 0.5), (845, 0.5), (762, 0.5), (700, 2.0)]}\nsource = 17\ntarget = 184\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": 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 = {78: [(902, 15.0), (837, 3.0)], 274: [(902, 15.0)], 894: [(902, 15.0)], 800: [(902, 15.0)], 837: [(489, 0.0)], 902: [(489, 0.0)], 489: [], 676: [(78, 5.0), (274, 10.0), (894, 5.0), (800, 2.0)]}\nsource = 676\ntarget = 489\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": 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 = {732: [(420, 0.0)], 899: [(658, 2.0)], 369: [(11, 2.0)], 11: [(504, 1.0), (658, 2.0)], 658: [(420, 0.0)], 504: [(420, 0.0)], 420: [], 479: [(732, 5.0), (899, 3.0), (369, 1.0)]}\nsource = 479\ntarget = 420\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": 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 = {817: [(280, 30.0)], 562: [(584, 0.0)], 807: [(584, 0.0)], 323: [(584, 0.0)], 781: [(584, 0.0)], 280: [(584, 0.0)], 584: [], 260: [(817, 60.0), (562, 30.0), (807, 15.0), (323, 20.0), (781, 60.0)]}\nsource = 260\ntarget = 584\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": 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 = {845: [(840, 15.0)], 582: [(845, 5.0)], 840: [(499, 0.0)], 230: [(499, 0.0)], 499: [], 354: [(582, 10.0), (230, 5.0)]}\nsource = 354\ntarget = 499\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": 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 = {74: [(343, 30.0)], 164: [(786, 2.0)], 328: [(786, 2.0)], 786: [(343, 30.0)], 343: [(222, 2.0)], 222: [(173, 0.0)], 173: [], 810: [(74, 10.0), (164, 5.0), (328, 15.0)]}\nsource = 810\ntarget = 173\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": 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 = {45: [(652, 0.0)], 94: [(730, 5.0)], 730: [(652, 0.0)], 652: [], 924: [(45, 30.0), (94, 10.0)]}\nsource = 924\ntarget = 652\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": 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 = {944: [(69, 10.0)], 69: [(719, 10.0)], 246: [(719, 10.0)], 719: [(215, 1.0)], 149: [(908, 0.0)], 215: [(149, 1.0)], 908: [], 9: [(944, 5.0), (246, 2.0)]}\nsource = 9\ntarget = 908\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": 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 = {587: [(486, 2.0)], 342: [(486, 2.0)], 486: [(188, 2.0)], 188: [(672, 0.0)], 678: [(672, 0.0)], 23: [(672, 0.0)], 594: [(672, 0.0)], 672: [], 33: [(587, 2.0), (342, 2.0), (678, 3.0), (23, 2.0), (594, 3.0)]}\nsource = 33\ntarget = 672\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": 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 = {513: [(668, 0.08), (688, 0.08)], 668: [(85, 0.0)], 688: [(85, 0.0)], 921: [(833, 0.17)], 833: [(235, 0.08)], 235: [(661, 0.08)], 661: [(513, 0.17)], 85: [], 486: [(921, 0.17)]}\nsource = 486\ntarget = 85\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.75, "source_answer": 0.75}
{"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 = {270: [(987, 30.0)], 737: [(656, 3.0)], 656: [(64, 5.0), (606, 5.0), (254, 10.0)], 987: [(737, 2.0)], 606: [(843, 0.0)], 64: [(843, 0.0)], 254: [(843, 0.0)], 843: [], 567: [(270, 5.0)]}\nsource = 567\ntarget = 843\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": 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 = {694: [(503, 0.0)], 150: [(503, 0.0)], 638: [(437, 10.0)], 437: [(503, 0.0)], 503: [], 613: [(694, 60.0), (150, 30.0), (638, 34164000.0)]}\nsource = 613\ntarget = 503\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34164010.0, "source_answer": 34164010.0}
{"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 = {203: [(228, 3.0), (736, 10.0), (614, 8.0), (462, 5.0), (995, 3.0), (743, 3.0)], 462: [(907, 0.0)], 995: [(907, 0.0)], 736: [(907, 0.0)], 743: [(907, 0.0)], 614: [(907, 0.0)], 228: [(907, 0.0)], 907: [], 35: [(203, 30.0)]}\nsource = 35\ntarget = 907\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": 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 = {573: [(706, 0.0)], 532: [(706, 0.0)], 263: [(706, 0.0)], 903: [(532, 60.0), (573, 30.0)], 706: [], 53: [(263, 60.0), (903, 20160.0)]}\nsource = 53\ntarget = 706\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": 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 = {459: [(426, 5.0)], 20: [(426, 5.0)], 44: [(426, 5.0)], 280: [(426, 5.0)], 517: [(426, 5.0)], 344: [(426, 5.0)], 426: [(36, 0.0)], 36: [], 389: [(459, 5.0), (20, 2.0), (44, 3.0), (280, 3.0), (517, 3.0), (344, 1.0)]}\nsource = 389\ntarget = 36\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": 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 = {24: [(136, 20.0)], 682: [(136, 20.0)], 136: [(320, 0.0)], 320: [], 252: [(24, 30.0), (682, 15.0)]}\nsource = 252\ntarget = 320\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": 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 = {439: [(678, 10.0), (545, 14400.0), (842, 10.0)], 545: [(137, 0.0)], 842: [(137, 0.0)], 678: [(137, 0.0)], 137: [], 845: [(439, 120.0)]}\nsource = 845\ntarget = 137\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14520.0, "source_answer": 14520.0}
{"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 = {12: [(692, 10.0), (224, 5.0)], 692: [(668, 2.0)], 224: [(668, 2.0)], 668: [(525, 30.0)], 525: [(747, 2.0)], 747: [(648, 1.0)], 648: [(17, 0.0)], 17: [], 377: [(12, 5.0)]}\nsource = 377\ntarget = 17\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": 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 = {198: [(287, 60.0), (928, 15.0)], 287: [(960, 0.0)], 928: [(960, 0.0)], 960: [], 773: [(198, 30.0)]}\nsource = 773\ntarget = 960\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": 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 = {845: [(610, 0.0)], 357: [(610, 0.0)], 454: [(610, 0.0)], 354: [(610, 0.0)], 497: [(620, 30.0)], 620: [(217, 30.0)], 217: [(411, 60.0)], 411: [(610, 0.0)], 610: [], 253: [(845, 120.0), (357, 60.0), (454, 180.0), (354, 30.0), (497, 60.0)]}\nsource = 253\ntarget = 610\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": 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 = {767: [(52, 5.0)], 52: [(697, 10.0)], 308: [(697, 10.0)], 697: [(533, 1.0)], 533: [(426, 0.0)], 426: [], 272: [(767, 5.0), (308, 2.0)]}\nsource = 272\ntarget = 426\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": 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 = {727: [(789, 10.0), (412, 15.0)], 789: [(463, 0.0)], 412: [(463, 0.0)], 347: [(789, 10.0), (412, 15.0)], 463: [], 524: [(727, 5.0), (347, 15.0)]}\nsource = 524\ntarget = 463\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": 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 = {496: [(413, 1.0), (920, 2.0), (386, 1.0), (807, 1.0), (685, 1.0), (41, 2.0), (811, 1.0)], 41: [(494, 0.0)], 807: [(494, 0.0)], 386: [(494, 0.0)], 811: [(494, 0.0)], 685: [(494, 0.0)], 413: [(494, 0.0)], 920: [(494, 0.0)], 494: [], 826: [(496, 5.0)]}\nsource = 826\ntarget = 494\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": 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 = {40: [(736, 1.0)], 736: [(859, 0.0)], 686: [(859, 0.0)], 707: [(859, 0.0)], 66: [(859, 0.0)], 859: [], 359: [(40, 5.0), (686, 2.0), (707, 1.0), (66, 3.0)]}\nsource = 359\ntarget = 859\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": 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 = {917: [(446, 1.0), (927, 1.0)], 446: [(967, 2.0)], 927: [(967, 2.0)], 967: [(322, 1.0)], 322: [(488, 1.0)], 488: [(863, 1.0)], 863: [(148, 5.0)], 148: [(822, 0.0)], 822: [], 710: [(917, 2.0)]}\nsource = 710\ntarget = 822\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": 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 = {371: [(728, 2.0)], 994: [(728, 2.0)], 728: [(967, 60.0)], 967: [(141, 0.0)], 141: [], 506: [(371, 5.0), (994, 10.0)]}\nsource = 506\ntarget = 141\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 72.0, "source_answer": 72.0}
{"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 = {792: [(523, 0.0)], 532: [(523, 0.0)], 187: [(928, 5.0), (341, 5.0)], 341: [(216, 10.0)], 928: [(216, 10.0)], 216: [(523, 0.0)], 120: [(354, 10.0)], 354: [(523, 0.0)], 761: [(523, 0.0)], 523: [], 449: [(792, 5.0), (532, 10.0), (187, 15.0), (120, 15.0), (761, 5.0)]}\nsource = 449\ntarget = 523\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": 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 = {191: [(649, 15.0)], 282: [(29, 15.0)], 29: [(289, 30.0)], 289: [(366, 10.0)], 366: [(649, 15.0)], 649: [(411, 20.0)], 411: [(177, 10.0)], 559: [(177, 10.0)], 177: [(831, 0.0)], 831: [], 84: [(191, 10.0), (282, 10.0), (559, 8.0)]}\nsource = 84\ntarget = 831\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": 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 = {800: [(218, 4.0)], 270: [(765, 0.0)], 934: [(218, 4.0)], 689: [(218, 4.0)], 495: [(218, 4.0)], 104: [(218, 4.0)], 218: [(216, 3.0)], 216: [(843, 3.0)], 843: [(190, 2.0)], 190: [(270, 5.0)], 765: [], 133: [(800, 5.0), (934, 3.0), (689, 2.0), (495, 3.0), (104, 3.0)]}\nsource = 133\ntarget = 765\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": 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 = {10: [(443, 1.0), (660, 1.0), (209, 2.0)], 245: [(854, 2.0)], 443: [(700, 2.0), (829, 2.0), (742, 1.0)], 209: [(700, 2.0), (829, 2.0), (742, 1.0)], 660: [(700, 2.0), (829, 2.0), (742, 1.0)], 829: [(635, 3.0)], 700: [(635, 3.0)], 742: [(635, 3.0)], 635: [(245, 1.0)], 854: [(954, 0.0)], 954: [], 492: [(10, 2.0)]}\nsource = 492\ntarget = 954\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": 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 = {141: [(274, 3.0)], 652: [(274, 3.0)], 970: [(274, 3.0)], 435: [(274, 3.0)], 399: [(274, 3.0)], 274: [(61, 10.0)], 61: [(846, 0.0)], 846: [], 644: [(141, 5.0), (652, 3.0), (970, 2.0), (435, 4.0), (399, 1.0)]}\nsource = 644\ntarget = 846\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": 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 = {748: [(747, 15.0)], 574: [(748, 5.0)], 747: [(730, 60.0)], 730: [(24, 5.0)], 24: [(621, 0.0)], 757: [(748, 5.0)], 935: [(748, 5.0)], 127: [(748, 5.0)], 151: [(748, 5.0)], 816: [(748, 5.0)], 621: [], 449: [(757, 15.0), (935, 30.0), (127, 45.0), (151, 40.0), (816, 35.0), (574, 50.0)]}\nsource = 449\ntarget = 621\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": 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 = {49: [(603, 2.0)], 603: [(160, 10.0)], 160: [(252, 5.0)], 252: [(885, 0.0)], 1: [(885, 0.0)], 885: [], 433: [(49, 5.0), (1, 2.0)]}\nsource = 433\ntarget = 885\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": 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 = {969: [(803, 25.0)], 960: [(803, 25.0)], 803: [(149, 0.0)], 808: [(281, 1.0)], 465: [(589, 1.0)], 589: [(892, 10.0)], 892: [(281, 1.0)], 338: [(247, 3.0)], 247: [(378, 2.0)], 378: [(960, 1.0)], 281: [(960, 1.0)], 149: [], 746: [(969, 1.0), (808, 1.0), (465, 2.0), (338, 2.0)]}\nsource = 746\ntarget = 149\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": 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 = {491: [(594, 2.0), (98, 5.0)], 716: [(296, 10.0)], 296: [(834, 0.0)], 64: [(594, 2.0)], 594: [(786, 10.0), (817, 5.0)], 703: [(98, 5.0)], 98: [(786, 10.0), (817, 5.0)], 786: [(693, 60.0)], 817: [(693, 60.0)], 693: [(563, 180.0)], 563: [(716, 10.0)], 834: [], 432: [(491, 5.0), (64, 5.0), (703, 7.0)]}\nsource = 432\ntarget = 834\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 282.0, "source_answer": 282.0}
{"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 = {726: [(230, 5.0)], 401: [(400, 3.0)], 519: [(901, 0.0)], 230: [(171, 2.0)], 168: [(171, 2.0)], 171: [(494, 5.0)], 494: [(205, 2.0)], 205: [(763, 5.0)], 763: [(372, 2.0)], 372: [(401, 2.0)], 400: [(519, 1.0)], 901: [], 742: [(726, 5.0), (168, 3.0)]}\nsource = 742\ntarget = 901\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": 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 = {622: [(790, 15.0)], 146: [(737, 60.0)], 737: [(229, 0.0)], 47: [(790, 15.0)], 864: [(790, 15.0)], 790: [(57, 10.0)], 57: [(319, 10.0)], 319: [(435, 30.0)], 435: [(255, 15.0)], 255: [(877, 5.0)], 877: [(146, 10.0)], 229: [], 473: [(622, 10.0), (47, 10.0), (864, 5.0)]}\nsource = 473\ntarget = 229\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": 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 = {992: [(796, 0.0)], 410: [(796, 0.0)], 837: [(796, 0.0)], 644: [(796, 0.0)], 568: [(796, 0.0)], 612: [(796, 0.0)], 520: [(812, 2.0)], 54: [(812, 2.0)], 812: [(796, 0.0)], 796: [], 714: [(992, 1.0), (410, 1.0), (837, 1.0), (644, 1.0), (568, 1.0), (612, 1.0), (520, 1.0), (54, 1.0)]}\nsource = 714\ntarget = 796\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": 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 = {988: [(357, 10.0)], 800: [(532, 5.0), (988, 5.0)], 548: [(355, 10.0)], 355: [(134, 5.0)], 134: [(482, 10.0)], 99: [(429, 2.0)], 429: [(539, 10.0)], 539: [(419, 2.0)], 419: [(369, 0.0)], 357: [(142, 5.0)], 142: [(381, 5.0)], 381: [(250, 10.0)], 532: [(250, 10.0)], 250: [(99, 2.0)], 482: [(652, 10.0)], 652: [(800, 5.0), (922, 10.0)], 922: [(988, 5.0), (532, 5.0)], 369: [], 978: [(548, 15.0)]}\nsource = 978\ntarget = 369\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 111.0, "source_answer": 111.0}
{"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 = {264: [(831, 0.5)], 831: [(62, 1.0)], 62: [(141, 1.0)], 141: [(23, 0.5), (283, 1.0)], 283: [(397, 0.75)], 23: [(397, 0.75)], 397: [(402, 0.0)], 402: [], 408: [(264, 0.5)]}\nsource = 408\ntarget = 402\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.75, "source_answer": 4.75}
{"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 = {15: [(61, 0.0)], 814: [(894, 1.0)], 894: [(61, 0.0)], 61: [], 553: [(15, 5.0), (814, 2.0)]}\nsource = 553\ntarget = 61\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": 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 = {170: [(76, 0.0)], 408: [(966, 2.0)], 966: [(76, 0.0)], 76: [], 656: [(170, 5.0), (408, 10.0)]}\nsource = 656\ntarget = 76\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": 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 = {272: [(714, 60.0), (950, 15.0)], 714: [(301, 0.0)], 950: [(301, 0.0)], 301: [], 3: [(272, 30.0)]}\nsource = 3\ntarget = 301\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": 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 = {267: [(754, 43200.0)], 40: [(698, 0.0)], 754: [(698, 0.0)], 698: [], 427: [(267, 5.0), (40, 20160.0)]}\nsource = 427\ntarget = 698\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43205.0, "source_answer": 43205.0}
{"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 = {481: [(570, 2628000.0)], 865: [(570, 2628000.0)], 570: [(763, 0.0)], 763: [], 302: [(481, 2628000.0), (865, 2102400.0)]}\nsource = 302\ntarget = 763\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": 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 = {906: [(422, 3.0), (793, 3.0)], 422: [(339, 0.0)], 793: [(339, 0.0)], 339: [], 341: [(906, 5.0)]}\nsource = 341\ntarget = 339\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": 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 = {166: [(528, 0.0)], 457: [(476, 3.0)], 476: [(528, 0.0)], 528: [], 540: [(166, 10.0), (457, 5.0)]}\nsource = 540\ntarget = 528\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": 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 = {42: [(617, 0.0)], 631: [(42, 5.0)], 329: [(42, 5.0)], 617: [], 763: [(631, 10.0), (329, 15.0)]}\nsource = 763\ntarget = 617\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": 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 = {248: [(973, 0.0)], 388: [(973, 0.0)], 677: [(388, 3.0), (248, 5.0)], 973: [], 73: [(677, 4.0)]}\nsource = 73\ntarget = 973\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": 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 = {830: [(853, 10.0)], 499: [(372, 15.0)], 853: [(372, 15.0)], 372: [(875, 0.0)], 875: [], 993: [(830, 5.0), (499, 30.0)]}\nsource = 993\ntarget = 875\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": 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 = {514: [(791, 15.0), (63, 30.0)], 63: [(487, 10080.0)], 487: [(204, 0.0)], 791: [(204, 0.0)], 204: [], 818: [(514, 15.0)]}\nsource = 818\ntarget = 204\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": 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 = {832: [(923, 3.0)], 923: [(933, 3.0), (615, 2.0)], 615: [(925, 0.0)], 933: [(925, 0.0)], 925: [], 28: [(832, 2.0)]}\nsource = 28\ntarget = 925\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": 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 = {715: [(631, 1.0)], 33: [(631, 1.0)], 631: [(270, 1.0)], 270: [(951, 0.0)], 951: [], 225: [(715, 1.0), (33, 1.0)]}\nsource = 225\ntarget = 951\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": 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 = {742: [(277, 2.0)], 85: [(277, 2.0)], 277: [(328, 0.0)], 328: [], 226: [(742, 5.0), (85, 10.0)]}\nsource = 226\ntarget = 328\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": 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 = {101: [(917, 0.0)], 797: [(101, 5.0)], 175: [(917, 0.0)], 592: [(917, 0.0)], 917: [], 487: [(797, 10.0), (175, 5.0), (592, 5.0)]}\nsource = 487\ntarget = 917\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": 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 = {494: [(705, 1.0)], 335: [(705, 1.0)], 705: [(155, 0.0)], 155: [], 483: [(494, 1.0), (335, 2.0)]}\nsource = 483\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": 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 = {863: [(678, 0.0)], 691: [(896, 2.0)], 896: [(678, 0.0)], 875: [(678, 0.0)], 678: [], 913: [(863, 5.0), (691, 15.0), (875, 10.0)]}\nsource = 913\ntarget = 678\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": 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 = {52: [(642, 0.0)], 152: [(873, 15.0)], 873: [(642, 0.0)], 147: [(642, 0.0)], 642: [], 903: [(52, 30.0), (152, 45.0), (147, 15.0)]}\nsource = 903\ntarget = 642\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": 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 = {954: [(977, 10.0), (471, 30.0)], 977: [(74, 0.0)], 471: [(74, 0.0)], 74: [], 769: [(954, 20.0)]}\nsource = 769\ntarget = 74\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": 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 = {586: [(153, 0.5)], 153: [(709, 5.0)], 709: [(322, 10.0), (790, 10.0)], 322: [(122, 5.0)], 790: [(122, 5.0)], 122: [(301, 0.0)], 301: [], 899: [(586, 0.5)]}\nsource = 899\ntarget = 301\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": 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 = {29: [(416, 0.08), (678, 0.08)], 416: [(986, 0.08)], 678: [(457, 0.08)], 986: [(239, 0.08)], 457: [(762, 0.08)], 239: [(811, 0.0)], 762: [(811, 0.0)], 811: [], 357: [(29, 0.5)]}\nsource = 357\ntarget = 811\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.74, "source_answer": 0.74}
{"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 = {843: [(434, 0.5), (863, 0.17)], 434: [(327, 0.02)], 863: [(327, 0.02)], 327: [(427, 0.17)], 427: [(637, 0.17)], 637: [(511, 0.0)], 511: [], 486: [(843, 1.0)]}\nsource = 486\ntarget = 511\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.86, "source_answer": 1.86}
{"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 = {963: [(365, 0.42), (439, 0.75), (422, 0.42)], 365: [(411, 1.0)], 422: [(411, 1.0)], 439: [(576, 0.17)], 411: [(871, 1.0)], 576: [(411, 1.0)], 871: [(853, 0.0)], 853: [], 29: [(963, 1.0)]}\nsource = 29\ntarget = 853\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.92, "source_answer": 3.92}
{"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 = {938: [(232, 1.0)], 232: [(51, 15.0)], 51: [(551, 5.0), (40, 5.0)], 551: [(344, 15.0)], 40: [(344, 15.0)], 344: [(855, 0.0)], 855: [], 37: [(938, 15.0)]}\nsource = 37\ntarget = 855\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": 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 = {864: [(980, 5.0)], 424: [(980, 5.0)], 980: [(963, 1.0)], 963: [(361, 1.0)], 361: [(561, 0.0)], 561: [], 413: [(864, 5.0), (424, 15.0)]}\nsource = 413\ntarget = 561\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": 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 = {798: [(906, 15.0)], 906: [(590, 0.0)], 152: [(364, 30.0)], 364: [(217, 15.0)], 217: [(70, 30.0)], 70: [(906, 15.0)], 590: [], 402: [(798, 15.0), (152, 15.0)]}\nsource = 402\ntarget = 590\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": 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 = {135: [(293, 35.0)], 293: [(939, 30.0), (990, 10.0)], 990: [(681, 30.0)], 939: [(959, 0.0)], 681: [(959, 0.0)], 959: [], 149: [(135, 15.0)]}\nsource = 149\ntarget = 959\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": 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 = {344: [(240, 10.0), (311, 5.0)], 240: [(109, 5.0)], 311: [(109, 5.0)], 109: [(795, 5.0)], 795: [(960, 10.0)], 960: [(375, 2.0)], 375: [(104, 0.0)], 104: [], 417: [(344, 5.0)]}\nsource = 417\ntarget = 104\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": 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 = {481: [(476, 2.0), (268, 10.0), (77, 0.05)], 476: [(98, 0.03)], 98: [(973, 0.0)], 268: [(847, 1.0)], 77: [(658, 1.0)], 847: [(98, 0.03)], 658: [(973, 0.0)], 973: [], 323: [(481, 30.0)]}\nsource = 323\ntarget = 973\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.03, "source_answer": 41.03}
{"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 = {565: [(218, 5.0)], 218: [(58, 5.0)], 58: [(745, 10.0)], 745: [(151, 30.0), (829, 60.0)], 829: [(65, 0.0)], 151: [(65, 0.0)], 65: [], 438: [(565, 20.0)]}\nsource = 438\ntarget = 65\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": 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 = {293: [(578, 1.0), (986, 0.5)], 578: [(201, 2.0)], 986: [(201, 2.0)], 201: [(102, 1.0)], 102: [(745, 0.0)], 745: [], 41: [(293, 1.0)]}\nsource = 41\ntarget = 745\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": 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 = {685: [(533, 5.0), (291, 5.0)], 533: [(986, 10.0)], 291: [(988, 3.0)], 986: [(840, 3.0)], 988: [(840, 3.0)], 840: [(134, 2.0)], 134: [(6, 0.0)], 6: [], 966: [(685, 5.0)]}\nsource = 966\ntarget = 6\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": 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 = {473: [(848, 10.0), (361, 15.0)], 848: [(201, 15.0)], 201: [(939, 1.0)], 939: [(180, 0.0)], 361: [(201, 15.0)], 180: [], 776: [(473, 15.0)]}\nsource = 776\ntarget = 180\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46.0, "source_answer": 46.0}
{"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 = {275: [(273, 120.0)], 273: [(69, 5.0)], 69: [(663, 5.0), (129, 5.0)], 663: [(615, 43200.0)], 129: [(615, 43200.0)], 615: [(162, 43200.0)], 162: [(849, 0.0)], 849: [], 44: [(275, 60.0)]}\nsource = 44\ntarget = 849\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86590.0, "source_answer": 86590.0}
{"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 = {469: [(475, 2880.0)], 475: [(538, 1440.0), (952, 28800.0), (372, 30.0), (616, 10.0), (256, 20.0)], 952: [(687, 0.0)], 538: [(687, 0.0)], 616: [(687, 0.0)], 372: [(687, 0.0)], 256: [(687, 0.0)], 687: [], 371: [(469, 432000.0)]}\nsource = 371\ntarget = 687\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 463680.0, "source_answer": 463680.0}
{"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 = {581: [(466, 1.0)], 101: [(466, 1.0)], 466: [(106, 0.17)], 106: [(169, 0.17)], 567: [(169, 0.17)], 169: [(164, 0.0)], 164: [], 242: [(581, 1.0), (101, 0.5), (567, 0.5)]}\nsource = 242\ntarget = 164\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.34, "source_answer": 2.34}
{"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 = {292: [(823, 15.0)], 835: [(823, 15.0)], 823: [(133, 1.0)], 133: [(990, 5.0)], 990: [(724, 0.0)], 724: [], 774: [(292, 5.0), (835, 10.0)]}\nsource = 774\ntarget = 724\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": 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 = {977: [(369, 1.0)], 227: [(369, 1.0)], 369: [(859, 0.58)], 859: [(386, 0.25)], 386: [(345, 0.0)], 345: [], 533: [(977, 1.0), (227, 1.0)]}\nsource = 533\ntarget = 345\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.83, "source_answer": 2.83}
{"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 = {125: [(139, 300.0), (482, 300.0)], 139: [(403, 120.0)], 482: [(403, 120.0)], 403: [(22, 120.0)], 22: [(470, 0.0)], 470: [], 377: [(125, 10.0)]}\nsource = 377\ntarget = 470\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 550.0, "source_answer": 550.0}
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