listRecentPaths

Prompt:
Implement the Python class `RecentPathManager` described below.

Class description:
The ``RecentPathManager`` is a simple class which provides access to a list of recently loaded files, and can notify registered listeners when that list changes. See the :attr:`recentPathManager` singleton instance.

Method signatures and docstrings:
- def recordPath(self, path): Adds the given ``path`` to the recent files list.
- def listRecentPaths(self): Returns a list of recently loaded files.

Implement the Python class `RecentPathManager` described below.

Class description:
The ``RecentPathManager`` is a simple class which provides access to a list of recently loaded files, and can notify registered listeners when that list changes. See the :attr:`recentPathManager` singleton instance.

Method signatures and docstrings:
- def recordPath(self, path): Adds the given ``path`` to the recent files list.
- def listRecentPaths(self): Returns a list of recently loaded files.

<|skeleton|>
class RecentPathManager:
    """The ``RecentPathManager`` is a simple class which provides access to a list of recently loaded files, and can notify registered listeners when that list changes. See the :attr:`recentPathManager` singleton instance."""

    def recordPath(self, path):
        """Adds the given ``path`` to the recent files list."""
        <|body_0|>

    def listRecentPaths(self):
        """Returns a list of recently loaded files."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        recent = self.listRecentPaths()
        if path in recent:
            return
        recent.append(path)
        if len(recent) > 10:
            recent = recent[-10:]
        recent = op.pathsep.join(recent)
        fslsettings.write('fsleyes.recentFiles', recent)
        self.notify()
<|end_body_0|>

<|body_start_1|>
        recent = fslsettings.read('fsleyes.recentFiles', None)
        if recent is None:
            recent = []
        else:
            recent = recent.split(op.pathsep)
        return [f for f in recent if op.exists(f)]
<|end_body_1|>

Solution:
class RecentPathManager:
    """The ``RecentPathManager`` is a simple class which provides access to a list of recently loaded files, and can notify registered listeners when that list changes. See the :attr:`recentPathManager` singleton instance."""

    def recordPath(self, path):
        """Adds the given ``path`` to the recent files list."""
        recent = self.listRecentPaths()
        if path in recent:
            return
        recent.append(path)
        if len(recent) > 10:
            recent = recent[-10:]
        recent = op.pathsep.join(recent)
        fslsettings.write('fsleyes.recentFiles', recent)
        self.notify()

    def listRecentPaths(self):
        """Returns a list of recently loaded files."""
        recent = fslsettings.read('fsleyes.recentFiles', None)
        if recent is None:
            recent = []
        else:
            recent = recent.split(op.pathsep)
        return [f for f in recent if op.exists(f)]


extensions

Prompt:
Implement the Python class `AlphaExpensive` described below.

Class description:
Compute α but requires storing individual gradients.

Method signatures and docstrings:
- def extensions(self, global_step): Return list of BackPACK extensions required for the computation. Args: global_step (int): The current iteration number. Returns: list: (Potentially empty) list with required BackPACK quantities.
- def _fetch_values(self, params, batch_loss, pos, global_step): Fetch values for quadratic fit. Return as dictionary. The entry "search_dir" is only initialized if ``pos`` is ``"start"``.

Implement the Python class `AlphaExpensive` described below.

Class description:
Compute α but requires storing individual gradients.

Method signatures and docstrings:
- def extensions(self, global_step): Return list of BackPACK extensions required for the computation. Args: global_step (int): The current iteration number. Returns: list: (Potentially empty) list with required BackPACK quantities.
- def _fetch_values(self, params, batch_loss, pos, global_step): Fetch values for quadratic fit. Return as dictionary. The entry "search_dir" is only initialized if ``pos`` is ``"start"``.

<|skeleton|>
class AlphaExpensive:
    """Compute α but requires storing individual gradients."""

    def extensions(self, global_step):
        """Return list of BackPACK extensions required for the computation. Args: global_step (int): The current iteration number. Returns: list: (Potentially empty) list with required BackPACK quantities."""
        <|body_0|>

    def _fetch_values(self, params, batch_loss, pos, global_step):
        """Fetch values for quadratic fit. Return as dictionary. The entry "search_dir" is only initialized if ``pos`` is ``"start"``."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ext = []
        if self._is_position(global_step, 'start') or self._is_position(global_step, 'end'):
            ext.append(extensions.BatchGrad())
        return ext
<|end_body_0|>

<|body_start_1|>
        info = {}
        if pos in ['start', 'end']:
            info['f'] = batch_loss.item()
            info['var_f'] = get_individual_losses(global_step).var().item()
            info['params'] = {id(p): p.data.clone().detach() for p in params}
            info['grad'] = {id(p): p.grad.data.clone().detach() for p in params}
            batch_size = get_batch_size(global_step)
            info['batch_grad'] = {id(p): batch_size * p.grad_batch.data.clone().detach() for p in params}
        else:
            raise ValueError(f"Invalid position '{pos}'. Expect {self._positions}.")
        if pos == 'end':
            start_params, _ = self._get_info('params', end=False)
            end_params = info['params']
            search_dir = [end_params[key] - start_params[key] for key in start_params.keys()]
            for info_dict in [self._start_info, info]:
                grad = [info_dict['grad'][key] for key in start_params.keys()]
                batch_grad = [info_dict['batch_grad'][key] for key in start_params.keys()]
                info_dict['df'] = _projected_gradient(grad, search_dir)
                info_dict['var_df'] = _exact_variance(batch_grad, search_dir)
        return info
<|end_body_1|>

Solution:
class AlphaExpensive:
    """Compute α but requires storing individual gradients."""

    def extensions(self, global_step):
        """Return list of BackPACK extensions required for the computation. Args: global_step (int): The current iteration number. Returns: list: (Potentially empty) list with required BackPACK quantities."""
        ext = []
        if self._is_position(global_step, 'start') or self._is_position(global_step, 'end'):
            ext.append(extensions.BatchGrad())
        return ext

    def _fetch_values(self, params, batch_loss, pos, global_step):
        """Fetch values for quadratic fit. Return as dictionary. The entry "search_dir" is only initialized if ``pos`` is ``"start"``."""
        info = {}
        if pos in ['start', 'end']:
            info['f'] = batch_loss.item()
            info['var_f'] = get_individual_losses(global_step).var().item()
            info['params'] = {id(p): p.data.clone().detach() for p in params}
            info['grad'] = {id(p): p.grad.data.clone().detach() for p in params}
            batch_size = get_batch_size(global_step)
            info['batch_grad'] = {id(p): batch_size * p.grad_batch.data.clone().detach() for p in params}
        else:
            raise ValueError(f"Invalid position '{pos}'. Expect {self._positions}.")
        if pos == 'end':
            start_params, _ = self._get_info('params', end=False)
            end_params = info['params']
            search_dir = [end_params[key] - start_params[key] for key in start_params.keys()]
            for info_dict in [self._start_info, info]:
                grad = [info_dict['grad'][key] for key in start_params.keys()]
                batch_grad = [info_dict['batch_grad'][key] for key in start_params.keys()]
                info_dict['df'] = _projected_gradient(grad, search_dir)
                info_dict['var_df'] = _exact_variance(batch_grad, search_dir)
        return info


longestPalindrome

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: DP 视频：https://www.youtube.com/watch?v=UflHuQj6MVA&t=441s 代码：https://www.geeksforgeeks.org/longest-palindrome-substring-set-1/ 思想： 先判断最长的子串是否为 1，2，及大于 3 max_length = 1, max_str = s[0], dp[i][i] = 1 max_length = 2, max_str = s[i:i+2], dp[i][i+1] = 1 max_length >= 3, max_str = s[i:i+k], k is the length of substring k 的范围是 [3, n+1] 表示整体往 dp 的右上方向遍历，结合视频中的那张图理解 i 从 0 开始，直到 n-k+1 行。也就是 i 从 第 0 行开始，斜向下遍历（以下是 dp table, 可以结合一下视频） 3 ( 0 2 ) ( 1 3 ) ( 2 4 ) ( 3 5 ) ( 4 6 ) ( 5 7 ) 4 ( 0 3 ) ( 1 4 ) ( 2 5 ) ( 3 6 ) ( 4 7 ) 5 ( 0 4 ) ( 1 5 ) ( 2 6 ) ( 3 7 ) 6 ( 0 5 ) ( 1 6 ) ( 2 7 ) 7 ( 0 6 ) ( 1 7 ) 8 ( 0 7 ) 生成 dp table 用来记录状态 状态转移方程：dp[i+1][j-1] and s[i] == s[j] 这里借用了一个大小为 M * N 二维的 dp table Time O(n^
- def longestPalindrome2(self, s: str) -> str: 扩展中心法 Expand Around Center 1. 遍历 str 中的点，以每个点为中心分别进行奇数、偶数开始向外扩展 2. 记录下扩展后得到的最大 str 3. 注意 extend 函数推出的时间点 这个解法比上一种解法好理解很多，速度也快，重点掌握这个

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: DP 视频：https://www.youtube.com/watch?v=UflHuQj6MVA&t=441s 代码：https://www.geeksforgeeks.org/longest-palindrome-substring-set-1/ 思想： 先判断最长的子串是否为 1，2，及大于 3 max_length = 1, max_str = s[0], dp[i][i] = 1 max_length = 2, max_str = s[i:i+2], dp[i][i+1] = 1 max_length >= 3, max_str = s[i:i+k], k is the length of substring k 的范围是 [3, n+1] 表示整体往 dp 的右上方向遍历，结合视频中的那张图理解 i 从 0 开始，直到 n-k+1 行。也就是 i 从 第 0 行开始，斜向下遍历（以下是 dp table, 可以结合一下视频） 3 ( 0 2 ) ( 1 3 ) ( 2 4 ) ( 3 5 ) ( 4 6 ) ( 5 7 ) 4 ( 0 3 ) ( 1 4 ) ( 2 5 ) ( 3 6 ) ( 4 7 ) 5 ( 0 4 ) ( 1 5 ) ( 2 6 ) ( 3 7 ) 6 ( 0 5 ) ( 1 6 ) ( 2 7 ) 7 ( 0 6 ) ( 1 7 ) 8 ( 0 7 ) 生成 dp table 用来记录状态 状态转移方程：dp[i+1][j-1] and s[i] == s[j] 这里借用了一个大小为 M * N 二维的 dp table Time O(n^
- def longestPalindrome2(self, s: str) -> str: 扩展中心法 Expand Around Center 1. 遍历 str 中的点，以每个点为中心分别进行奇数、偶数开始向外扩展 2. 记录下扩展后得到的最大 str 3. 注意 extend 函数推出的时间点 这个解法比上一种解法好理解很多，速度也快，重点掌握这个

<|skeleton|>
class Solution:

    def longestPalindrome(self, s: str) -> str:
        """DP 视频：https://www.youtube.com/watch?v=UflHuQj6MVA&t=441s 代码：https://www.geeksforgeeks.org/longest-palindrome-substring-set-1/ 思想： 先判断最长的子串是否为 1，2，及大于 3 max_length = 1, max_str = s[0], dp[i][i] = 1 max_length = 2, max_str = s[i:i+2], dp[i][i+1] = 1 max_length >= 3, max_str = s[i:i+k], k is the length of substring k 的范围是 [3, n+1] 表示整体往 dp 的右上方向遍历，结合视频中的那张图理解 i 从 0 开始，直到 n-k+1 行。也就是 i 从 第 0 行开始，斜向下遍历（以下是 dp table, 可以结合一下视频） 3 ( 0 2 ) ( 1 3 ) ( 2 4 ) ( 3 5 ) ( 4 6 ) ( 5 7 ) 4 ( 0 3 ) ( 1 4 ) ( 2 5 ) ( 3 6 ) ( 4 7 ) 5 ( 0 4 ) ( 1 5 ) ( 2 6 ) ( 3 7 ) 6 ( 0 5 ) ( 1 6 ) ( 2 7 ) 7 ( 0 6 ) ( 1 7 ) 8 ( 0 7 ) 生成 dp table 用来记录状态 状态转移方程：dp[i+1][j-1] and s[i] == s[j] 这里借用了一个大小为 M * N 二维的 dp table Time O(n^"""
        <|body_0|>

    def longestPalindrome2(self, s: str) -> str:
        """扩展中心法 Expand Around Center 1. 遍历 str 中的点，以每个点为中心分别进行奇数、偶数开始向外扩展 2. 记录下扩展后得到的最大 str 3. 注意 extend 函数推出的时间点 这个解法比上一种解法好理解很多，速度也快，重点掌握这个"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n = len(s)
        dp = [[0 for _ in range(n)] for _ in range(n)]
        max_length = 1
        max_str = s[0]
        for i in range(n):
            dp[i][i] = 1
        for i in range(n - 1):
            if s[i] == s[i + 1]:
                dp[i][i + 1] = 1
                max_str = s[i:i + 2]
                max_length = 2
        for k in range(3, n + 1):
            for i in range(n - k + 1):
                j = i + k - 1
                if dp[i + 1][j - 1] and s[i] == s[j]:
                    dp[i][j] = 1
                    if k > max_length:
                        max_str = s[i:j + 1]
                        max_length = k
        return max_str
<|end_body_0|>

<|body_start_1|>
        n = len(s)
        res = s[0]

        def extend(i, j):
            while i >= 0 and j < len(s) and (s[i] == s[j]):
                i -= 1
                j += 1
            return s[i + 1:j]
        for i in range(n - 1):
            e1 = extend(i, i)
            e2 = extend(i, i + 1)
            if max(len(e1), len(e2)) > len(res):
                res = e1 if len(e1) > len(e2) else e2
        return res
<|end_body_1|>

Solution:
class Solution:
    def longestPalindrome(self, s: str) -> str:
        """DP 视频：https://www.youtube.com/watch?v=UflHuQj6MVA&t=441s 代码：https://www.geeksforgeeks.org/longest-palindrome-substring-set-1/ 思想： 先判断最长的子串是否为 1，2，及大于 3 max_length = 1, max_str = s[0], dp[i][i] = 1 max_length = 2, max_str = s[i:i+2], dp[i][i+1] = 1 max_length >= 3, max_str = s[i:i+k], k is the length of substring k 的范围是 [3, n+1] 表示整体往 dp 的右上方向遍历，结合视频中的那张图理解 i 从 0 开始，直到 n-k+1 行。也就是 i 从 第 0 行开始，斜向下遍历（以下是 dp table, 可以结合一下视频） 3 ( 0 2 ) ( 1 3 ) ( 2 4 ) ( 3 5 ) ( 4 6 ) ( 5 7 ) 4 ( 0 3 ) ( 1 4 ) ( 2 5 ) ( 3 6 ) ( 4 7 ) 5 ( 0 4 ) ( 1 5 ) ( 2 6 ) ( 3 7 ) 6 ( 0 5 ) ( 1 6 ) ( 2 7 ) 7 ( 0 6 ) ( 1 7 ) 8 ( 0 7 ) 生成 dp table 用来记录状态 状态转移方程：dp[i+1][j-1] and s[i] == s[j] 这里借用了一个大小为 M * N 二维的 dp table Time O(n^"""
        n = len(s)
        dp = [[0 for _ in range(n)] for _ in range(n)]
        max_length = 1
        max_str = s[0]
        for i in range(n):
            dp[i][i] = 1
        for i in range(n - 1):
            if s[i] == s[i + 1]:
                dp[i][i + 1] = 1
                max_str = s[i:i + 2]
                max_length = 2
        for k in range(3, n + 1):
            for i in range(n - k + 1):
                j = i + k - 1
                if dp[i + 1][j - 1] and s[i] == s[j]:
                    dp[i][j] = 1
                    if k > max_length:
                        max_str = s[i:j + 1]
                        max_length = k
        return max_str

    def longestPalindrome2(self, s: str) -> str:
        """扩展中心法 Expand Around Center 1. 遍历 str 中的点，以每个点为中心分别进行奇数、偶数开始向外扩展 2. 记录下扩展后得到的最大 str 3. 注意 extend 函数推出的时间点 这个解法比上一种解法好理解很多，速度也快，重点掌握这个"""
        n = len(s)
        res = s[0]

        def extend(i, j):
            while i >= 0 and j < len(s) and (s[i] == s[j]):
                i -= 1
                j += 1
            return s[i + 1:j]
        for i in range(n - 1):
            e1 = extend(i, i)
            e2 = extend(i, i + 1)
            if max(len(e1), len(e2)) > len(res):
                res = e1 if len(e1) > len(e2) else e2
        return res


list

Prompt:
Implement the Python class `FilesView` described below.

Class description:
This view is the endpoint for retrieving source/product files

Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, request): Retrieves the batches and returns them in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user
- def _list_v6(self, request): Retrieves a list of files based on filters and returns it in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user

Implement the Python class `FilesView` described below.

Class description:
This view is the endpoint for retrieving source/product files

Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, request): Retrieves the batches and returns them in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user
- def _list_v6(self, request): Retrieves a list of files based on filters and returns it in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user

<|skeleton|>
class FilesView:
    """This view is the endpoint for retrieving source/product files"""

    def get_serializer_class(self):
        """Returns the appropriate serializer based off the requests version of the REST API"""
        <|body_0|>

    def list(self, request):
        """Retrieves the batches and returns them in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user"""
        <|body_1|>

    def _list_v6(self, request):
        """Retrieves a list of files based on filters and returns it in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if self.request.version == 'v6':
            return ScaleFileSerializerV6
        elif self.request.version == 'v7':
            return ScaleFileSerializerV6
<|end_body_0|>

<|body_start_1|>
        if request.version == 'v6':
            return self._list_v6(request)
        elif request.version == 'v7':
            return self._list_v6(request)
        raise Http404()
<|end_body_1|>

<|body_start_2|>
        countries = rest_util.parse_string_list(request, 'countries', required=False)
        data_started = rest_util.parse_timestamp(request, 'data_started', required=False)
        data_ended = rest_util.parse_timestamp(request, 'data_ended', required=False)
        rest_util.check_time_range(data_started, data_ended)
        source_started = rest_util.parse_timestamp(request, 'source_started', required=False)
        source_ended = rest_util.parse_timestamp(request, 'source_ended', required=False)
        rest_util.check_time_range(source_started, source_ended)
        source_sensor_classes = rest_util.parse_string_list(request, 'source_sensor_class', required=False)
        source_sensors = rest_util.parse_string_list(request, 'source_sensor', required=False)
        source_collections = rest_util.parse_string_list(request, 'source_collection', required=False)
        source_tasks = rest_util.parse_string_list(request, 'source_task', required=False)
        mod_started = rest_util.parse_timestamp(request, 'modified_started', required=False)
        mod_ended = rest_util.parse_timestamp(request, 'modified_ended', required=False)
        rest_util.check_time_range(mod_started, mod_ended)
        job_type_ids = rest_util.parse_int_list(request, 'job_type_id', required=False)
        job_type_names = rest_util.parse_string_list(request, 'job_type_name', required=False)
        job_ids = rest_util.parse_int_list(request, 'job_id', required=False)
        file_names = rest_util.parse_string_list(request, 'file_name', required=False)
        job_outputs = rest_util.parse_string_list(request, 'job_output', required=False)
        recipe_ids = rest_util.parse_int_list(request, 'recipe_id', required=False)
        recipe_type_ids = rest_util.parse_int_list(request, 'recipe_type_id', required=False)
        recipe_nodes = rest_util.parse_string_list(request, 'recipe_node', required=False)
        batch_ids = rest_util.parse_int_list(request, 'batch_id', required=False)
        order = rest_util.parse_string_list(request, 'order', required=False)
        files = ScaleFile.objects.filter_files(data_started=data_started, data_ended=data_ended, source_started=source_started, source_ended=source_ended, source_sensor_classes=source_sensor_classes, source_sensors=source_sensors, source_collections=source_collections, source_tasks=source_tasks, mod_started=mod_started, mod_ended=mod_ended, job_type_ids=job_type_ids, job_type_names=job_type_names, job_ids=job_ids, file_names=file_names, job_outputs=job_outputs, recipe_ids=recipe_ids, recipe_type_ids=recipe_type_ids, recipe_nodes=recipe_nodes, batch_ids=batch_ids, order=order, countries=countries)
        page = self.paginate_queryset(files)
        serializer = self.get_serializer(page, many=True)
        return self.get_paginated_response(serializer.data)
<|end_body_2|>

Solution:
class FilesView:
    """This view is the endpoint for retrieving source/product files"""

    def get_serializer_class(self):
        """Returns the appropriate serializer based off the requests version of the REST API"""
        if self.request.version == 'v6':
            return ScaleFileSerializerV6
        elif self.request.version == 'v7':
            return ScaleFileSerializerV6

    def list(self, request):
        """Retrieves the batches and returns them in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user"""
        if request.version == 'v6':
            return self._list_v6(request)
        elif request.version == 'v7':
            return self._list_v6(request)
        raise Http404()

    def _list_v6(self, request):
        """Retrieves a list of files based on filters and returns it in JSON form :param request: the HTTP GET request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user"""
        countries = rest_util.parse_string_list(request, 'countries', required=False)
        data_started = rest_util.parse_timestamp(request, 'data_started', required=False)
        data_ended = rest_util.parse_timestamp(request, 'data_ended', required=False)
        rest_util.check_time_range(data_started, data_ended)
        source_started = rest_util.parse_timestamp(request, 'source_started', required=False)
        source_ended = rest_util.parse_timestamp(request, 'source_ended', required=False)
        rest_util.check_time_range(source_started, source_ended)
        source_sensor_classes = rest_util.parse_string_list(request, 'source_sensor_class', required=False)
        source_sensors = rest_util.parse_string_list(request, 'source_sensor', required=False)
        source_collections = rest_util.parse_string_list(request, 'source_collection', required=False)
        source_tasks = rest_util.parse_string_list(request, 'source_task', required=False)
        mod_started = rest_util.parse_timestamp(request, 'modified_started', required=False)
        mod_ended = rest_util.parse_timestamp(request, 'modified_ended', required=False)
        rest_util.check_time_range(mod_started, mod_ended)
        job_type_ids = rest_util.parse_int_list(request, 'job_type_id', required=False)
        job_type_names = rest_util.parse_string_list(request, 'job_type_name', required=False)
        job_ids = rest_util.parse_int_list(request, 'job_id', required=False)
        file_names = rest_util.parse_string_list(request, 'file_name', required=False)
        job_outputs = rest_util.parse_string_list(request, 'job_output', required=False)
        recipe_ids = rest_util.parse_int_list(request, 'recipe_id', required=False)
        recipe_type_ids = rest_util.parse_int_list(request, 'recipe_type_id', required=False)
        recipe_nodes = rest_util.parse_string_list(request, 'recipe_node', required=False)
        batch_ids = rest_util.parse_int_list(request, 'batch_id', required=False)
        order = rest_util.parse_string_list(request, 'order', required=False)
        files = ScaleFile.objects.filter_files(data_started=data_started, data_ended=data_ended, source_started=source_started, source_ended=source_ended, source_sensor_classes=source_sensor_classes, source_sensors=source_sensors, source_collections=source_collections, source_tasks=source_tasks, mod_started=mod_started, mod_ended=mod_ended, job_type_ids=job_type_ids, job_type_names=job_type_names, job_ids=job_ids, file_names=file_names, job_outputs=job_outputs, recipe_ids=recipe_ids, recipe_type_ids=recipe_type_ids, recipe_nodes=recipe_nodes, batch_ids=batch_ids, order=order, countries=countries)
        page = self.paginate_queryset(files)
        serializer = self.get_serializer(page, many=True)
        return self.get_paginated_response(serializer.data)


query

Prompt:
Implement the Python class `RTDFacetedSearch` described below.

Class description:
Implement the RTDFacetedSearch class.

Method signatures and docstrings:
- def __init__(self, user, **kwargs): Pass in a user in order to filter search results by privacy. .. warning:: The `self.user` and `self.filter_by_user` attributes aren't currently used on the .org, but are used on the .com.
- def query(self, search, query): Add query part to ``search`` when needed. Also: * Adds SimpleQueryString instead of default query. * Adds HTML encoding of results to avoid XSS issues.

Implement the Python class `RTDFacetedSearch` described below.

Class description:
Implement the RTDFacetedSearch class.

Method signatures and docstrings:
- def __init__(self, user, **kwargs): Pass in a user in order to filter search results by privacy. .. warning:: The `self.user` and `self.filter_by_user` attributes aren't currently used on the .org, but are used on the .com.
- def query(self, search, query): Add query part to ``search`` when needed. Also: * Adds SimpleQueryString instead of default query. * Adds HTML encoding of results to avoid XSS issues.

<|skeleton|>
class RTDFacetedSearch:

    def __init__(self, user, **kwargs):
        """Pass in a user in order to filter search results by privacy. .. warning:: The `self.user` and `self.filter_by_user` attributes aren't currently used on the .org, but are used on the .com."""
        <|body_0|>

    def query(self, search, query):
        """Add query part to ``search`` when needed. Also: * Adds SimpleQueryString instead of default query. * Adds HTML encoding of results to avoid XSS issues."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.user = user
        self.filter_by_user = kwargs.pop('filter_by_user', True)
        for facet in self.facets:
            if facet in kwargs:
                kwargs.setdefault('filters', {})[facet] = kwargs.pop(facet)
        for f in ALL_FACETS:
            if f in kwargs:
                del kwargs[f]
        log.info('Hacking Elastic to fix search connection pooling')
        self.using = Elasticsearch(**settings.ELASTICSEARCH_DSL['default'])
        super().__init__(**kwargs)
<|end_body_0|>

<|body_start_1|>
        search = search.highlight_options(encoder='html', number_of_fragments=3)
        search = search.source(exclude=['content', 'headers'])
        all_queries = []
        for operator in self.operators:
            query_string = SimpleQueryString(query=query, fields=self.fields, default_operator=operator)
            all_queries.append(query_string)
        bool_query = Bool(should=all_queries)
        search = search.query(bool_query)
        return search
<|end_body_1|>

Solution:
class RTDFacetedSearch:
    def __init__(self, user, **kwargs):
        """Pass in a user in order to filter search results by privacy. .. warning:: The `self.user` and `self.filter_by_user` attributes aren't currently used on the .org, but are used on the .com."""
        self.user = user
        self.filter_by_user = kwargs.pop('filter_by_user', True)
        for facet in self.facets:
            if facet in kwargs:
                kwargs.setdefault('filters', {})[facet] = kwargs.pop(facet)
        for f in ALL_FACETS:
            if f in kwargs:
                del kwargs[f]
        log.info('Hacking Elastic to fix search connection pooling')
        self.using = Elasticsearch(**settings.ELASTICSEARCH_DSL['default'])
        super().__init__(**kwargs)

    def query(self, search, query):
        """Add query part to ``search`` when needed. Also: * Adds SimpleQueryString instead of default query. * Adds HTML encoding of results to avoid XSS issues."""
        search = search.highlight_options(encoder='html', number_of_fragments=3)
        search = search.source(exclude=['content', 'headers'])
        all_queries = []
        for operator in self.operators:
            query_string = SimpleQueryString(query=query, fields=self.fields, default_operator=operator)
            all_queries.append(query_string)
        bool_query = Bool(should=all_queries)
        search = search.query(bool_query)
        return search


deserialize

Prompt:
Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

<|skeleton|>
class Codec:

    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        <|body_0|>

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not root:
            return '[]'
        data = []
        queue = [root]
        while queue:
            length = len(queue)
            for i in range(length):
                node = queue.pop(0)
                if node:
                    data.append(node.val)
                    queue.append(node.left)
                    queue.append(node.right)
                else:
                    data.append(None)
        while data[-1] is None:
            data.pop()
        return str(data)
<|end_body_0|>

<|body_start_1|>
        if data == '[]':
            return
        i, j = (1, 1)
        while data[i].isdigit() or data[i] == '-':
            i += 1
        root = TreeNode(int(data[j:i]))
        queue = [[root, 0]]
        while i < len(data):
            if data[i].isdigit() or data[i] == '-':
                j = i
                while i < len(data) and data[i].isdigit() or data[i] == '-':
                    i += 1
                new = TreeNode(int(data[j:i]))
                if not queue[0][1]:
                    queue[0][0].left = new
                    queue[0][1] += 1
                    queue.append([new, 0])
                else:
                    node, t = queue.pop(0)
                    node.right = new
                    queue.append([new, 0])
            elif data[i] == 'N':
                if not queue[0][1]:
                    queue[0][1] += 1
                else:
                    queue.pop(0)
                i += 4
            else:
                i += 1
        return root
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        if not root:
            return '[]'
        data = []
        queue = [root]
        while queue:
            length = len(queue)
            for i in range(length):
                node = queue.pop(0)
                if node:
                    data.append(node.val)
                    queue.append(node.left)
                    queue.append(node.right)
                else:
                    data.append(None)
        while data[-1] is None:
            data.pop()
        return str(data)

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        if data == '[]':
            return
        i, j = (1, 1)
        while data[i].isdigit() or data[i] == '-':
            i += 1
        root = TreeNode(int(data[j:i]))
        queue = [[root, 0]]
        while i < len(data):
            if data[i].isdigit() or data[i] == '-':
                j = i
                while i < len(data) and data[i].isdigit() or data[i] == '-':
                    i += 1
                new = TreeNode(int(data[j:i]))
                if not queue[0][1]:
                    queue[0][0].left = new
                    queue[0][1] += 1
                    queue.append([new, 0])
                else:
                    node, t = queue.pop(0)
                    node.right = new
                    queue.append([new, 0])
            elif data[i] == 'N':
                if not queue[0][1]:
                    queue[0][1] += 1
                else:
                    queue.pop(0)
                i += 4
            else:
                i += 1
        return root


Prompt:
Implement the Python class `TestApiPorpertySensitive` described below.

Class description:
Implement the TestApiPorpertySensitive class.

Method signatures and docstrings:
- def test_update_property(self): 参数: 修改类别为update，id和bank为true，其他为false 期望结果： 返回的识别信息列表有两个信息cn_id和cn_bank
- def test_default_property(self): 参数: 修改类别为defalut， 期望结果： 返回的识别信息列表有四个信息cn_id、cn_bank、cn_email、cn_telephon

Implement the Python class `TestApiPorpertySensitive` described below.

Class description:
Implement the TestApiPorpertySensitive class.

Method signatures and docstrings:
- def test_update_property(self): 参数: 修改类别为update，id和bank为true，其他为false 期望结果： 返回的识别信息列表有两个信息cn_id和cn_bank
- def test_default_property(self): 参数: 修改类别为defalut， 期望结果： 返回的识别信息列表有四个信息cn_id、cn_bank、cn_email、cn_telephon

<|skeleton|>
class TestApiPorpertySensitive:

    def test_update_property(self):
        """参数: 修改类别为update，id和bank为true，其他为false 期望结果： 返回的识别信息列表有两个信息cn_id和cn_bank"""
        <|body_0|>

    def test_default_property(self):
        """参数: 修改类别为defalut， 期望结果： 返回的识别信息列表有四个信息cn_id、cn_bank、cn_email、cn_telephon"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        global url
        sess = session()
        params = {'parameter': {'recognize_class': 'update', 'rate_grade': '高', 'update_propertiy': {'cn_id': 'true', 'cn_bank': 'true', 'cn_email': 'true', 'cn_date': 'false', 'cn_telephone': 'true', 'cn_passport': 'false'}}}
        s_get = sess.put(url, json=params)
        json_data = json.loads(s_get.content.decode('utf8'))
        print('修改：')
        print(json_data)
        status = json_data['status']
        self.assertEqual(status, 0)
        class_message = json_data['class_message']
        self.assertEqual(4, len(class_message))
        print(type(class_message))
        sess.close()
<|end_body_0|>

<|body_start_1|>
        global url
        sess = session()
        params = {'parameter': {'recognize_class': 'default', 'rate_grade': '高', 'update_propertiy': {'cn_id': 'true', 'cn_bank': 'true', 'cn_email': 'false', 'cn_date': 'false', 'cn_telephon': 'false', 'cn_passport': 'false'}}}
        s_get = sess.put(url, json=params)
        json_data = json.loads(s_get.content.decode('utf8'))
        print('默认')
        print(json_data)
        status = json_data['status']
        self.assertEqual(status, 0)
        class_message = json_data['class_message']
        self.assertEqual(4, len(class_message))
        print(type(class_message))
        sess.close()
<|end_body_1|>

Solution:
class TestApiPorpertySensitive:
    def test_update_property(self):
        """参数: 修改类别为update，id和bank为true，其他为false 期望结果： 返回的识别信息列表有两个信息cn_id和cn_bank"""
        global url
        sess = session()
        params = {'parameter': {'recognize_class': 'update', 'rate_grade': '高', 'update_propertiy': {'cn_id': 'true', 'cn_bank': 'true', 'cn_email': 'true', 'cn_date': 'false', 'cn_telephone': 'true', 'cn_passport': 'false'}}}
        s_get = sess.put(url, json=params)
        json_data = json.loads(s_get.content.decode('utf8'))
        print('修改：')
        print(json_data)
        status = json_data['status']
        self.assertEqual(status, 0)
        class_message = json_data['class_message']
        self.assertEqual(4, len(class_message))
        print(type(class_message))
        sess.close()

    def test_default_property(self):
        """参数: 修改类别为defalut， 期望结果： 返回的识别信息列表有四个信息cn_id、cn_bank、cn_email、cn_telephon"""
        global url
        sess = session()
        params = {'parameter': {'recognize_class': 'default', 'rate_grade': '高', 'update_propertiy': {'cn_id': 'true', 'cn_bank': 'true', 'cn_email': 'false', 'cn_date': 'false', 'cn_telephon': 'false', 'cn_passport': 'false'}}}
        s_get = sess.put(url, json=params)
        json_data = json.loads(s_get.content.decode('utf8'))
        print('默认')
        print(json_data)
        status = json_data['status']
        self.assertEqual(status, 0)
        class_message = json_data['class_message']
        self.assertEqual(4, len(class_message))
        print(type(class_message))
        sess.close()


sendRdiscReply

Prompt:
Implement the Python class `Ndisc` described below.

Class description:
Implement the Ndisc class.

Method signatures and docstrings:
- def sendNdiscRequest(logger, device, destination, count=3, timeout=1, quiet=False, firstReply=False, blocking=True): This function sends IPv6 neighbor discovery to a neighbour host Args: logger device - Name of network device where to send ARP REQUEST packets destination - destination IP to ping count - send ICMPv6 Neighbor Discovery X times timeout - specify a timeout, in seconds, before ndisc exits quiet - quiet output firstReply - Exit as soon as the first advertisement is received blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None
- def sendRdiscReply(logger, device, destination, count=3, quiet=False, blocking=True): This function sends IPv6 router discovery to a neighbour host Args: logger device - Name of network device where to send ICMPv6 Router Discovery packets destination - destination IP to ping count - send ICMPv6 Router Discovery X times quiet - quiet output blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None

Implement the Python class `Ndisc` described below.

Class description:
Implement the Ndisc class.

Method signatures and docstrings:
- def sendNdiscRequest(logger, device, destination, count=3, timeout=1, quiet=False, firstReply=False, blocking=True): This function sends IPv6 neighbor discovery to a neighbour host Args: logger device - Name of network device where to send ARP REQUEST packets destination - destination IP to ping count - send ICMPv6 Neighbor Discovery X times timeout - specify a timeout, in seconds, before ndisc exits quiet - quiet output firstReply - Exit as soon as the first advertisement is received blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None
- def sendRdiscReply(logger, device, destination, count=3, quiet=False, blocking=True): This function sends IPv6 router discovery to a neighbour host Args: logger device - Name of network device where to send ICMPv6 Router Discovery packets destination - destination IP to ping count - send ICMPv6 Router Discovery X times quiet - quiet output blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None

<|skeleton|>
class Ndisc:

    def sendNdiscRequest(logger, device, destination, count=3, timeout=1, quiet=False, firstReply=False, blocking=True):
        """This function sends IPv6 neighbor discovery to a neighbour host Args: logger device - Name of network device where to send ARP REQUEST packets destination - destination IP to ping count - send ICMPv6 Neighbor Discovery X times timeout - specify a timeout, in seconds, before ndisc exits quiet - quiet output firstReply - Exit as soon as the first advertisement is received blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None"""
        <|body_0|>

    def sendRdiscReply(logger, device, destination, count=3, quiet=False, blocking=True):
        """This function sends IPv6 router discovery to a neighbour host Args: logger device - Name of network device where to send ICMPv6 Router Discovery packets destination - destination IP to ping count - send ICMPv6 Router Discovery X times quiet - quiet output blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        args = [Ndisc.NDISC6_COMMAND_NAME, Ndisc.COUNT_OPTION, str(count), Ndisc.TIMEOUT_OPTION, str(int(timeout) * 1000)]
        if quiet is True:
            args.append(Ndisc.QUIET_OPTION)
        if firstReply is True:
            args.append(Ndisc.FIRST_REPLY_OPTION)
        args.append(destination)
        args.append(device)
        rc = Command.execute(logger, Ndisc.NDISC6_COMMAND_NAME, args, timeoutSec=timeout + 3, blocking=blocking)
        return rc
<|end_body_0|>

<|body_start_1|>
        args = [Ndisc.RDISC6_COMMAND_NAME, Ndisc.COUNT_OPTION, str(count)]
        if quiet is True:
            args.append(Ndisc.QUIET_OPTION)
        args.append(destination)
        args.append(device)
        rc = Command.execute(logger, Ndisc.RDISC6_COMMAND_NAME, args, blocking=blocking)
        return rc
<|end_body_1|>

Solution:
class Ndisc:
    def sendNdiscRequest(logger, device, destination, count=3, timeout=1, quiet=False, firstReply=False, blocking=True):
        """This function sends IPv6 neighbor discovery to a neighbour host Args: logger device - Name of network device where to send ARP REQUEST packets destination - destination IP to ping count - send ICMPv6 Neighbor Discovery X times timeout - specify a timeout, in seconds, before ndisc exits quiet - quiet output firstReply - Exit as soon as the first advertisement is received blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None"""
        args = [Ndisc.NDISC6_COMMAND_NAME, Ndisc.COUNT_OPTION, str(count), Ndisc.TIMEOUT_OPTION, str(int(timeout) * 1000)]
        if quiet is True:
            args.append(Ndisc.QUIET_OPTION)
        if firstReply is True:
            args.append(Ndisc.FIRST_REPLY_OPTION)
        args.append(destination)
        args.append(device)
        rc = Command.execute(logger, Ndisc.NDISC6_COMMAND_NAME, args, timeoutSec=timeout + 3, blocking=blocking)
        return rc

    def sendRdiscReply(logger, device, destination, count=3, quiet=False, blocking=True):
        """This function sends IPv6 router discovery to a neighbour host Args: logger device - Name of network device where to send ICMPv6 Router Discovery packets destination - destination IP to ping count - send ICMPv6 Router Discovery X times quiet - quiet output blocking - if True, waits for command to complete Return: tuple (rc, stdout, stderr) Raise: None"""
        args = [Ndisc.RDISC6_COMMAND_NAME, Ndisc.COUNT_OPTION, str(count)]
        if quiet is True:
            args.append(Ndisc.QUIET_OPTION)
        args.append(destination)
        args.append(device)
        rc = Command.execute(logger, Ndisc.RDISC6_COMMAND_NAME, args, blocking=blocking)
        return rc


Prompt:
Implement the Python class `ThreadStepRunner` described below.

Class description:
Implements thread which runs a test step

Method signatures and docstrings:
- def __init__(self, queue, execution_queue, context): Constructor
- def run(self): Runs the test step into a thread.

Implement the Python class `ThreadStepRunner` described below.

Class description:
Implements thread which runs a test step

Method signatures and docstrings:
- def __init__(self, queue, execution_queue, context): Constructor
- def run(self): Runs the test step into a thread.

<|skeleton|>
class ThreadStepRunner:
    """Implements thread which runs a test step"""

    def __init__(self, queue, execution_queue, context):
        """Constructor"""
        <|body_0|>

    def run(self):
        """Runs the test step into a thread."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        threading.Thread.__init__(self)
        self._queue = queue
        self._execution_queue = execution_queue
        self._context = context
<|end_body_0|>

<|body_start_1|>
        threading.Thread.run(self)
        test_step = self._queue.get()
        try:
            test_step.run(self._context)
            self._execution_queue.put((test_step.name, test_step.ts_verdict_msg))
        except Exception as ex:
            self._execution_queue.put(ex)
        finally:
            self._queue.task_done()
<|end_body_1|>

Solution:
class ThreadStepRunner:
    """Implements thread which runs a test step"""

    def __init__(self, queue, execution_queue, context):
        """Constructor"""
        threading.Thread.__init__(self)
        self._queue = queue
        self._execution_queue = execution_queue
        self._context = context

    def run(self):
        """Runs the test step into a thread."""
        threading.Thread.run(self)
        test_step = self._queue.get()
        try:
            test_step.run(self._context)
            self._execution_queue.put((test_step.name, test_step.ts_verdict_msg))
        except Exception as ex:
            self._execution_queue.put(ex)
        finally:
            self._queue.task_done()


Prompt:
Implement the Python class `GraphChildListCreateViewMixin` described below.

Class description:
A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object

Method signatures and docstrings:
- def list_(self, request, graph_id): Return all the children of a given graph
- def perform_create_(self, serializer): Add the graph to the child object.

Implement the Python class `GraphChildListCreateViewMixin` described below.

Class description:
A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object

Method signatures and docstrings:
- def list_(self, request, graph_id): Return all the children of a given graph
- def perform_create_(self, serializer): Add the graph to the child object.

<|skeleton|>
class GraphChildListCreateViewMixin:
    """A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object"""

    def list_(self, request, graph_id):
        """Return all the children of a given graph"""
        <|body_0|>

    def perform_create_(self, serializer):
        """Add the graph to the child object."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        queryset = self.get_child_qs(graph_id)
        serializer = self.get_serializer(queryset, many=True)
        return response.Response(serializer.data)
<|end_body_0|>

<|body_start_1|>
        graph_id = self.request.resolver_match.kwargs['graph_id']
        graph = self.get_graph(graph_id)
        serializer.save(graph=graph)
<|end_body_1|>

Solution:
class GraphChildListCreateViewMixin:
    """A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object"""

    def list_(self, request, graph_id):
        """Return all the children of a given graph"""
        queryset = self.get_child_qs(graph_id)
        serializer = self.get_serializer(queryset, many=True)
        return response.Response(serializer.data)

    def perform_create_(self, serializer):
        """Add the graph to the child object."""
        graph_id = self.request.resolver_match.kwargs['graph_id']
        graph = self.get_graph(graph_id)
        serializer.save(graph=graph)


syslog

Prompt:
Implement the Python class `GetLogCommands` described below.

Class description:
Get logging information.

Method signatures and docstrings:
- def errors(self): Get all of the errors from the log files.
- def syslog(self, num_entries=10): Get <num_entries> of the manila syslog events.

Implement the Python class `GetLogCommands` described below.

Class description:
Get logging information.

Method signatures and docstrings:
- def errors(self): Get all of the errors from the log files.
- def syslog(self, num_entries=10): Get <num_entries> of the manila syslog events.

<|skeleton|>
class GetLogCommands:
    """Get logging information."""

    def errors(self):
        """Get all of the errors from the log files."""
        <|body_0|>

    def syslog(self, num_entries=10):
        """Get <num_entries> of the manila syslog events."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        error_found = 0
        if CONF.log_dir:
            logs = [x for x in os.listdir(CONF.log_dir) if x.endswith('.log')]
            for file in logs:
                log_file = os.path.join(CONF.log_dir, file)
                lines = [line.strip() for line in open(log_file, 'r')]
                lines.reverse()
                print_name = 0
                for index, line in enumerate(lines):
                    if line.find(' ERROR ') > 0:
                        error_found += 1
                        if print_name == 0:
                            print(log_file + ':-')
                            print_name = 1
                        print('Line %d : %s' % (len(lines) - index, line))
        if error_found == 0:
            print('No errors in logfiles!')
<|end_body_0|>

<|body_start_1|>
        entries = int(num_entries)
        count = 0
        log_file = ''
        if os.path.exists('/var/log/syslog'):
            log_file = '/var/log/syslog'
        elif os.path.exists('/var/log/messages'):
            log_file = '/var/log/messages'
        else:
            print('Unable to find system log file!')
            sys.exit(1)
        lines = [line.strip() for line in open(log_file, 'r')]
        lines.reverse()
        print('Last %s manila syslog entries:-' % entries)
        for line in lines:
            if line.find('manila') > 0:
                count += 1
                print('%s' % line)
            if count == entries:
                break
        if count == 0:
            print('No manila entries in syslog!')
<|end_body_1|>

Solution:
class GetLogCommands:
    """Get logging information."""

    def errors(self):
        """Get all of the errors from the log files."""
        error_found = 0
        if CONF.log_dir:
            logs = [x for x in os.listdir(CONF.log_dir) if x.endswith('.log')]
            for file in logs:
                log_file = os.path.join(CONF.log_dir, file)
                lines = [line.strip() for line in open(log_file, 'r')]
                lines.reverse()
                print_name = 0
                for index, line in enumerate(lines):
                    if line.find(' ERROR ') > 0:
                        error_found += 1
                        if print_name == 0:
                            print(log_file + ':-')
                            print_name = 1
                        print('Line %d : %s' % (len(lines) - index, line))
        if error_found == 0:
            print('No errors in logfiles!')

    def syslog(self, num_entries=10):
        """Get <num_entries> of the manila syslog events."""
        entries = int(num_entries)
        count = 0
        log_file = ''
        if os.path.exists('/var/log/syslog'):
            log_file = '/var/log/syslog'
        elif os.path.exists('/var/log/messages'):
            log_file = '/var/log/messages'
        else:
            print('Unable to find system log file!')
            sys.exit(1)
        lines = [line.strip() for line in open(log_file, 'r')]
        lines.reverse()
        print('Last %s manila syslog entries:-' % entries)
        for line in lines:
            if line.find('manila') > 0:
                count += 1
                print('%s' % line)
            if count == entries:
                break
        if count == 0:
            print('No manila entries in syslog!')


Prompt:
Implement the Python class `PowerWallEntity` described below.

Class description:
Base class for powerwall entities.

Method signatures and docstrings:
- def __init__(self, coordinator, site_info, status, device_type, powerwalls_serial_numbers): Initialize the sensor.
- def device_info(self): Powerwall device info.

Implement the Python class `PowerWallEntity` described below.

Class description:
Base class for powerwall entities.

Method signatures and docstrings:
- def __init__(self, coordinator, site_info, status, device_type, powerwalls_serial_numbers): Initialize the sensor.
- def device_info(self): Powerwall device info.

<|skeleton|>
class PowerWallEntity:
    """Base class for powerwall entities."""

    def __init__(self, coordinator, site_info, status, device_type, powerwalls_serial_numbers):
        """Initialize the sensor."""
        <|body_0|>

    def device_info(self):
        """Powerwall device info."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(coordinator)
        self._site_info = site_info
        self._device_type = device_type
        self._version = status.version
        self.base_unique_id = '_'.join(powerwalls_serial_numbers)
<|end_body_0|>

<|body_start_1|>
        device_info = {'identifiers': {(DOMAIN, self.base_unique_id)}, 'name': self._site_info.site_name, 'manufacturer': MANUFACTURER}
        model = MODEL
        model += f' ({self._device_type.name})'
        device_info['model'] = model
        device_info['sw_version'] = self._version
        return device_info
<|end_body_1|>

Solution:
class PowerWallEntity:
    """Base class for powerwall entities."""

    def __init__(self, coordinator, site_info, status, device_type, powerwalls_serial_numbers):
        """Initialize the sensor."""
        super().__init__(coordinator)
        self._site_info = site_info
        self._device_type = device_type
        self._version = status.version
        self.base_unique_id = '_'.join(powerwalls_serial_numbers)

    def device_info(self):
        """Powerwall device info."""
        device_info = {'identifiers': {(DOMAIN, self.base_unique_id)}, 'name': self._site_info.site_name, 'manufacturer': MANUFACTURER}
        model = MODEL
        model += f' ({self._device_type.name})'
        device_info['model'] = model
        device_info['sw_version'] = self._version
        return device_info


Prompt:
Implement the Python class `Request` described below.

Class description:
Implementation of the 'Request' model. TODO: type model description here. Attributes: is_valid (string): TODO: type description here. browse_node_lookup_request (BrowseNodeLookupRequest): TODO: type description here. item_search_request (ItemSearchRequest): TODO: type description here. item_lookup_request (ItemLookupRequest): TODO: type description here. similarity_lookup_request (SimilarityLookupRequest): TODO: type description here. cart_get_request (CartGetRequest): TODO: type description here. cart_add_request (CartAddRequest): TODO: type description here. cart_create_request (CartCreateRequest): TODO: type description here. cart_modify_request (CartModifyRequest): TODO: type description

Method signatures and docstrings:
- def __init__(self, is_valid=None, browse_node_lookup_request=None, item_search_request=None, item_lookup_request=None, similarity_lookup_request=None, cart_get_request=None, cart_add_request=None, cart_create_request=None, cart_modify_request=None, cart_clear_request=None, errors=None): Constructor for the Request class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `Request` described below.

Class description:
Implementation of the 'Request' model. TODO: type model description here. Attributes: is_valid (string): TODO: type description here. browse_node_lookup_request (BrowseNodeLookupRequest): TODO: type description here. item_search_request (ItemSearchRequest): TODO: type description here. item_lookup_request (ItemLookupRequest): TODO: type description here. similarity_lookup_request (SimilarityLookupRequest): TODO: type description here. cart_get_request (CartGetRequest): TODO: type description here. cart_add_request (CartAddRequest): TODO: type description here. cart_create_request (CartCreateRequest): TODO: type description here. cart_modify_request (CartModifyRequest): TODO: type description

Method signatures and docstrings:
- def __init__(self, is_valid=None, browse_node_lookup_request=None, item_search_request=None, item_lookup_request=None, similarity_lookup_request=None, cart_get_request=None, cart_add_request=None, cart_create_request=None, cart_modify_request=None, cart_clear_request=None, errors=None): Constructor for the Request class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class Request:
    """Implementation of the 'Request' model. TODO: type model description here. Attributes: is_valid (string): TODO: type description here. browse_node_lookup_request (BrowseNodeLookupRequest): TODO: type description here. item_search_request (ItemSearchRequest): TODO: type description here. item_lookup_request (ItemLookupRequest): TODO: type description here. similarity_lookup_request (SimilarityLookupRequest): TODO: type description here. cart_get_request (CartGetRequest): TODO: type description here. cart_add_request (CartAddRequest): TODO: type description here. cart_create_request (CartCreateRequest): TODO: type description here. cart_modify_request (CartModifyRequest): TODO: type description"""

    def __init__(self, is_valid=None, browse_node_lookup_request=None, item_search_request=None, item_lookup_request=None, similarity_lookup_request=None, cart_get_request=None, cart_add_request=None, cart_create_request=None, cart_modify_request=None, cart_clear_request=None, errors=None):
        """Constructor for the Request class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.is_valid = is_valid
        self.browse_node_lookup_request = browse_node_lookup_request
        self.item_search_request = item_search_request
        self.item_lookup_request = item_lookup_request
        self.similarity_lookup_request = similarity_lookup_request
        self.cart_get_request = cart_get_request
        self.cart_add_request = cart_add_request
        self.cart_create_request = cart_create_request
        self.cart_modify_request = cart_modify_request
        self.cart_clear_request = cart_clear_request
        self.errors = errors
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        is_valid = dictionary.get('IsValid')
        browse_node_lookup_request = awsecommerceservice.models.browse_node_lookup_request.BrowseNodeLookupRequest.from_dictionary(dictionary.get('BrowseNodeLookupRequest')) if dictionary.get('BrowseNodeLookupRequest') else None
        item_search_request = awsecommerceservice.models.item_search_request.ItemSearchRequest.from_dictionary(dictionary.get('ItemSearchRequest')) if dictionary.get('ItemSearchRequest') else None
        item_lookup_request = awsecommerceservice.models.item_lookup_request.ItemLookupRequest.from_dictionary(dictionary.get('ItemLookupRequest')) if dictionary.get('ItemLookupRequest') else None
        similarity_lookup_request = awsecommerceservice.models.similarity_lookup_request.SimilarityLookupRequest.from_dictionary(dictionary.get('SimilarityLookupRequest')) if dictionary.get('SimilarityLookupRequest') else None
        cart_get_request = awsecommerceservice.models.cart_get_request.CartGetRequest.from_dictionary(dictionary.get('CartGetRequest')) if dictionary.get('CartGetRequest') else None
        cart_add_request = awsecommerceservice.models.cart_add_request.CartAddRequest.from_dictionary(dictionary.get('CartAddRequest')) if dictionary.get('CartAddRequest') else None
        cart_create_request = awsecommerceservice.models.cart_create_request.CartCreateRequest.from_dictionary(dictionary.get('CartCreateRequest')) if dictionary.get('CartCreateRequest') else None
        cart_modify_request = awsecommerceservice.models.cart_modify_request.CartModifyRequest.from_dictionary(dictionary.get('CartModifyRequest')) if dictionary.get('CartModifyRequest') else None
        cart_clear_request = awsecommerceservice.models.cart_clear_request.CartClearRequest.from_dictionary(dictionary.get('CartClearRequest')) if dictionary.get('CartClearRequest') else None
        errors = awsecommerceservice.models.errors.Errors.from_dictionary(dictionary.get('Errors')) if dictionary.get('Errors') else None
        return cls(is_valid, browse_node_lookup_request, item_search_request, item_lookup_request, similarity_lookup_request, cart_get_request, cart_add_request, cart_create_request, cart_modify_request, cart_clear_request, errors)
<|end_body_1|>

Solution:
class Request:
    """Implementation of the 'Request' model. TODO: type model description here. Attributes: is_valid (string): TODO: type description here. browse_node_lookup_request (BrowseNodeLookupRequest): TODO: type description here. item_search_request (ItemSearchRequest): TODO: type description here. item_lookup_request (ItemLookupRequest): TODO: type description here. similarity_lookup_request (SimilarityLookupRequest): TODO: type description here. cart_get_request (CartGetRequest): TODO: type description here. cart_add_request (CartAddRequest): TODO: type description here. cart_create_request (CartCreateRequest): TODO: type description here. cart_modify_request (CartModifyRequest): TODO: type description"""

    def __init__(self, is_valid=None, browse_node_lookup_request=None, item_search_request=None, item_lookup_request=None, similarity_lookup_request=None, cart_get_request=None, cart_add_request=None, cart_create_request=None, cart_modify_request=None, cart_clear_request=None, errors=None):
        """Constructor for the Request class"""
        self.is_valid = is_valid
        self.browse_node_lookup_request = browse_node_lookup_request
        self.item_search_request = item_search_request
        self.item_lookup_request = item_lookup_request
        self.similarity_lookup_request = similarity_lookup_request
        self.cart_get_request = cart_get_request
        self.cart_add_request = cart_add_request
        self.cart_create_request = cart_create_request
        self.cart_modify_request = cart_modify_request
        self.cart_clear_request = cart_clear_request
        self.errors = errors

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        is_valid = dictionary.get('IsValid')
        browse_node_lookup_request = awsecommerceservice.models.browse_node_lookup_request.BrowseNodeLookupRequest.from_dictionary(dictionary.get('BrowseNodeLookupRequest')) if dictionary.get('BrowseNodeLookupRequest') else None
        item_search_request = awsecommerceservice.models.item_search_request.ItemSearchRequest.from_dictionary(dictionary.get('ItemSearchRequest')) if dictionary.get('ItemSearchRequest') else None
        item_lookup_request = awsecommerceservice.models.item_lookup_request.ItemLookupRequest.from_dictionary(dictionary.get('ItemLookupRequest')) if dictionary.get('ItemLookupRequest') else None
        similarity_lookup_request = awsecommerceservice.models.similarity_lookup_request.SimilarityLookupRequest.from_dictionary(dictionary.get('SimilarityLookupRequest')) if dictionary.get('SimilarityLookupRequest') else None
        cart_get_request = awsecommerceservice.models.cart_get_request.CartGetRequest.from_dictionary(dictionary.get('CartGetRequest')) if dictionary.get('CartGetRequest') else None
        cart_add_request = awsecommerceservice.models.cart_add_request.CartAddRequest.from_dictionary(dictionary.get('CartAddRequest')) if dictionary.get('CartAddRequest') else None
        cart_create_request = awsecommerceservice.models.cart_create_request.CartCreateRequest.from_dictionary(dictionary.get('CartCreateRequest')) if dictionary.get('CartCreateRequest') else None
        cart_modify_request = awsecommerceservice.models.cart_modify_request.CartModifyRequest.from_dictionary(dictionary.get('CartModifyRequest')) if dictionary.get('CartModifyRequest') else None
        cart_clear_request = awsecommerceservice.models.cart_clear_request.CartClearRequest.from_dictionary(dictionary.get('CartClearRequest')) if dictionary.get('CartClearRequest') else None
        errors = awsecommerceservice.models.errors.Errors.from_dictionary(dictionary.get('Errors')) if dictionary.get('Errors') else None
        return cls(is_valid, browse_node_lookup_request, item_search_request, item_lookup_request, similarity_lookup_request, cart_get_request, cart_add_request, cart_create_request, cart_modify_request, cart_clear_request, errors)


post

Prompt:
Implement the Python class `StrategyQueryHandler` described below.

Class description:
Implement the StrategyQueryHandler class.

Method signatures and docstrings:
- def get(self, app, name): get a specific strategy @API summary: get a specific strategy notes: get an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json
- def delete(self, app, name): delete strategy by its name and app @API summary: delete strategy by its name and app notes: delete an strategy by its name and app tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json
- def post(self, app, name): add or modify a specific strategy @API summary: add or modify a specific strategy notes: add or modify an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy - name: strategy in: body required: true type: json description: the json of the strategy produces: - application/json

Implement the Python class `StrategyQueryHandler` described below.

Class description:
Implement the StrategyQueryHandler class.

Method signatures and docstrings:
- def get(self, app, name): get a specific strategy @API summary: get a specific strategy notes: get an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json
- def delete(self, app, name): delete strategy by its name and app @API summary: delete strategy by its name and app notes: delete an strategy by its name and app tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json
- def post(self, app, name): add or modify a specific strategy @API summary: add or modify a specific strategy notes: add or modify an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy - name: strategy in: body required: true type: json description: the json of the strategy produces: - application/json

<|skeleton|>
class StrategyQueryHandler:

    def get(self, app, name):
        """get a specific strategy @API summary: get a specific strategy notes: get an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json"""
        <|body_0|>

    def delete(self, app, name):
        """delete strategy by its name and app @API summary: delete strategy by its name and app notes: delete an strategy by its name and app tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json"""
        <|body_1|>

    def post(self, app, name):
        """add or modify a specific strategy @API summary: add or modify a specific strategy notes: add or modify an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy - name: strategy in: body required: true type: json description: the json of the strategy produces: - application/json"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.set_header('content-type', 'application/json')
        try:
            strategy = StrategyCustDao().get_strategy_by_app_and_name(app, name).get_dict()
            if self.group.is_root():
                self.process_error(-1, 'root用户组没有权限查询策略')
            elif self.group.is_manager():
                self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': [strategy]}))
            else:
                view_strategy = GroupPermissionDao().get_group_strategy_block(self.group.id)
                be_block_groups_ids = view_strategy.get('be_blocked', [])
                if strategy['group_id'] not in be_block_groups_ids:
                    strategy.pop('score')
                else:
                    strategy = {}
                self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': [strategy]}))
        except Exception as err:
            logger.error(err)
            self.finish(json_dumps({'status': -1, 'msg': 'fail to get data from database'}))
<|end_body_0|>

<|body_start_1|>
        self.set_header('content-type', 'application/json')
        try:
            StrategyCustDao().delete_strategy_by_app_and_name(app, name)
            self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': []}))
        except Exception as err:
            logger.error(err)
            self.finish(json_dumps({'status': -1, 'msg': 'fail to get data from database'}))
<|end_body_1|>

<|body_start_2|>
        self.set_header('content-type', 'application/json')
        body = self.request.body
        body['version'] = millis_now()
        body['group_id'] = self.group.id
        try:
            new_strategy = Strategy.from_json(body)
        except Exception as err:
            return self.process_error(400, 'invalid request content: {}'.format(err.message))
        try:
            gen_variables_from_strategy(new_strategy, effective_check=False)
            new_strategy.version = millis_now()
            StrategyCustDao().add_strategy(new_strategy)
            self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': []}))
        except Exception as err:
            logger.error(err)
            self.process_error(500, 'fail to add meta to database')
<|end_body_2|>

Solution:
class StrategyQueryHandler:
    def get(self, app, name):
        """get a specific strategy @API summary: get a specific strategy notes: get an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json"""
        self.set_header('content-type', 'application/json')
        try:
            strategy = StrategyCustDao().get_strategy_by_app_and_name(app, name).get_dict()
            if self.group.is_root():
                self.process_error(-1, 'root用户组没有权限查询策略')
            elif self.group.is_manager():
                self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': [strategy]}))
            else:
                view_strategy = GroupPermissionDao().get_group_strategy_block(self.group.id)
                be_block_groups_ids = view_strategy.get('be_blocked', [])
                if strategy['group_id'] not in be_block_groups_ids:
                    strategy.pop('score')
                else:
                    strategy = {}
                self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': [strategy]}))
        except Exception as err:
            logger.error(err)
            self.finish(json_dumps({'status': -1, 'msg': 'fail to get data from database'}))

    def delete(self, app, name):
        """delete strategy by its name and app @API summary: delete strategy by its name and app notes: delete an strategy by its name and app tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy produces: - application/json"""
        self.set_header('content-type', 'application/json')
        try:
            StrategyCustDao().delete_strategy_by_app_and_name(app, name)
            self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': []}))
        except Exception as err:
            logger.error(err)
            self.finish(json_dumps({'status': -1, 'msg': 'fail to get data from database'}))

    def post(self, app, name):
        """add or modify a specific strategy @API summary: add or modify a specific strategy notes: add or modify an strategy according to its app and name tags: - nebula parameters: - name: app in: path required: true type: string description: the app of the strategy - name: name in: path required: true type: string description: the name of the strategy - name: strategy in: body required: true type: json description: the json of the strategy produces: - application/json"""
        self.set_header('content-type', 'application/json')
        body = self.request.body
        body['version'] = millis_now()
        body['group_id'] = self.group.id
        try:
            new_strategy = Strategy.from_json(body)
        except Exception as err:
            return self.process_error(400, 'invalid request content: {}'.format(err.message))
        try:
            gen_variables_from_strategy(new_strategy, effective_check=False)
            new_strategy.version = millis_now()
            StrategyCustDao().add_strategy(new_strategy)
            self.finish(json_dumps({'status': 200, 'msg': 'ok', 'values': []}))
        except Exception as err:
            logger.error(err)
            self.process_error(500, 'fail to add meta to database')


forward

Prompt:
Implement the Python class `GroverBondVocabPredictor` described below.

Class description:
Layer for learning contextual information for bonds. The layer is used in Grover architecture to learn contextual information of a bond by predicting the context of a bond from the bond embedding in a multi-class classification setting. The contextual information of a bond are encoded as strings (ex: '(DOUBLE-STEREONONE-NONE)_C-(SINGLE-STEREONONE-NONE)2'). Example ------- >>> from deepchem.models.torch_models.grover_layers import GroverBondVocabPredictor >>> num_bonds = 20 >>> in_features, vocab_size = 16, 10 >>> layer = GroverBondVocabPredictor(vocab_size, in_features) >>> embedding = torch.randn(num_bonds * 2, in_features) >>> result = layer(embedding) >>> result.shape torch.Size([20, 10])

Method signatures and docstrings:
- def __init__(self, vocab_size: int, in_features: int=128): Initializes GroverBondVocabPredictor Parameters ---------- vocab_size: int Size of vocabulary, used for number of classes in prediction. in_features: int, default: 128 Input feature size of bond embeddings.
- def forward(self, embeddings): Parameters ---------- embeddings: torch.Tensor bond embeddings of shape (num_bond, in_features) Returns ------- logits: torch.Tensor the prediction for each bond, (num_bond, vocab_size)

Implement the Python class `GroverBondVocabPredictor` described below.

Class description:
Layer for learning contextual information for bonds. The layer is used in Grover architecture to learn contextual information of a bond by predicting the context of a bond from the bond embedding in a multi-class classification setting. The contextual information of a bond are encoded as strings (ex: '(DOUBLE-STEREONONE-NONE)_C-(SINGLE-STEREONONE-NONE)2'). Example ------- >>> from deepchem.models.torch_models.grover_layers import GroverBondVocabPredictor >>> num_bonds = 20 >>> in_features, vocab_size = 16, 10 >>> layer = GroverBondVocabPredictor(vocab_size, in_features) >>> embedding = torch.randn(num_bonds * 2, in_features) >>> result = layer(embedding) >>> result.shape torch.Size([20, 10])

Method signatures and docstrings:
- def __init__(self, vocab_size: int, in_features: int=128): Initializes GroverBondVocabPredictor Parameters ---------- vocab_size: int Size of vocabulary, used for number of classes in prediction. in_features: int, default: 128 Input feature size of bond embeddings.
- def forward(self, embeddings): Parameters ---------- embeddings: torch.Tensor bond embeddings of shape (num_bond, in_features) Returns ------- logits: torch.Tensor the prediction for each bond, (num_bond, vocab_size)

<|skeleton|>
class GroverBondVocabPredictor:
    """Layer for learning contextual information for bonds. The layer is used in Grover architecture to learn contextual information of a bond by predicting the context of a bond from the bond embedding in a multi-class classification setting. The contextual information of a bond are encoded as strings (ex: '(DOUBLE-STEREONONE-NONE)_C-(SINGLE-STEREONONE-NONE)2'). Example ------- >>> from deepchem.models.torch_models.grover_layers import GroverBondVocabPredictor >>> num_bonds = 20 >>> in_features, vocab_size = 16, 10 >>> layer = GroverBondVocabPredictor(vocab_size, in_features) >>> embedding = torch.randn(num_bonds * 2, in_features) >>> result = layer(embedding) >>> result.shape torch.Size([20, 10])"""

    def __init__(self, vocab_size: int, in_features: int=128):
        """Initializes GroverBondVocabPredictor Parameters ---------- vocab_size: int Size of vocabulary, used for number of classes in prediction. in_features: int, default: 128 Input feature size of bond embeddings."""
        <|body_0|>

    def forward(self, embeddings):
        """Parameters ---------- embeddings: torch.Tensor bond embeddings of shape (num_bond, in_features) Returns ------- logits: torch.Tensor the prediction for each bond, (num_bond, vocab_size)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(GroverBondVocabPredictor, self).__init__()
        self.linear = nn.Linear(in_features, vocab_size)
        self.linear_rev = nn.Linear(in_features, vocab_size)
        self.logsoftmax = nn.LogSoftmax(dim=1)
<|end_body_0|>

<|body_start_1|>
        nm_bonds = embeddings.shape[0]
        ids1 = list(range(0, nm_bonds, 2))
        ids2 = list(range(1, nm_bonds, 2))
        logits = self.linear(embeddings[ids1]) + self.linear_rev(embeddings[ids2])
        return self.logsoftmax(logits)
<|end_body_1|>

Solution:
class GroverBondVocabPredictor:
    """Layer for learning contextual information for bonds. The layer is used in Grover architecture to learn contextual information of a bond by predicting the context of a bond from the bond embedding in a multi-class classification setting. The contextual information of a bond are encoded as strings (ex: '(DOUBLE-STEREONONE-NONE)_C-(SINGLE-STEREONONE-NONE)2'). Example ------- >>> from deepchem.models.torch_models.grover_layers import GroverBondVocabPredictor >>> num_bonds = 20 >>> in_features, vocab_size = 16, 10 >>> layer = GroverBondVocabPredictor(vocab_size, in_features) >>> embedding = torch.randn(num_bonds * 2, in_features) >>> result = layer(embedding) >>> result.shape torch.Size([20, 10])"""

    def __init__(self, vocab_size: int, in_features: int=128):
        """Initializes GroverBondVocabPredictor Parameters ---------- vocab_size: int Size of vocabulary, used for number of classes in prediction. in_features: int, default: 128 Input feature size of bond embeddings."""
        super(GroverBondVocabPredictor, self).__init__()
        self.linear = nn.Linear(in_features, vocab_size)
        self.linear_rev = nn.Linear(in_features, vocab_size)
        self.logsoftmax = nn.LogSoftmax(dim=1)

    def forward(self, embeddings):
        """Parameters ---------- embeddings: torch.Tensor bond embeddings of shape (num_bond, in_features) Returns ------- logits: torch.Tensor the prediction for each bond, (num_bond, vocab_size)"""
        nm_bonds = embeddings.shape[0]
        ids1 = list(range(0, nm_bonds, 2))
        ids2 = list(range(1, nm_bonds, 2))
        logits = self.linear(embeddings[ids1]) + self.linear_rev(embeddings[ids2])
        return self.logsoftmax(logits)


Prompt:
Implement the Python class `Block3DSpatialDict` described below.

Class description:
Class for tracking blocks in the XZ-plane.

Method signatures and docstrings:
- def key_for_bucket(self, key): Partition keys into chunk-sized buckets.
- def keys_near(self, key, radius): Get all bucket keys "near" this key. This method may return a generator.

Implement the Python class `Block3DSpatialDict` described below.

Class description:
Class for tracking blocks in the XZ-plane.

Method signatures and docstrings:
- def key_for_bucket(self, key): Partition keys into chunk-sized buckets.
- def keys_near(self, key, radius): Get all bucket keys "near" this key. This method may return a generator.

<|skeleton|>
class Block3DSpatialDict:
    """Class for tracking blocks in the XZ-plane."""

    def key_for_bucket(self, key):
        """Partition keys into chunk-sized buckets."""
        <|body_0|>

    def keys_near(self, key, radius):
        """Get all bucket keys "near" this key. This method may return a generator."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            return (int(key[0] // 16), int(key[1] // 16), int(key[2] // 16))
        except ValueError:
            return KeyError("Key %s isn't usable here!" % repr(key))
<|end_body_0|>

<|body_start_1|>
        minx, innerx = divmod(key[0], 16)
        miny, innery = divmod(key[1], 16)
        minz, innerz = divmod(key[2], 16)
        minx = int(minx)
        miny = int(miny)
        minz = int(minz)
        maxx = minx + 1
        maxy = miny + 1
        maxz = minz + 1
        if innerx <= radius:
            minx -= 1
        if innery <= radius:
            miny -= 1
        if innerz <= radius:
            minz -= 1
        if innerx + radius >= 16:
            maxx += 1
        if innery + radius >= 16:
            maxy += 1
        if innerz + radius >= 16:
            maxz += 1
        expand = int(radius // 16)
        minx -= expand
        miny -= expand
        minz -= expand
        maxx += expand
        maxy += expand
        maxz += expand
        return product(xrange(minx, maxx), xrange(miny, maxy), xrange(minz, maxz))
<|end_body_1|>

Solution:
class Block3DSpatialDict:
    """Class for tracking blocks in the XZ-plane."""

    def key_for_bucket(self, key):
        """Partition keys into chunk-sized buckets."""
        try:
            return (int(key[0] // 16), int(key[1] // 16), int(key[2] // 16))
        except ValueError:
            return KeyError("Key %s isn't usable here!" % repr(key))

    def keys_near(self, key, radius):
        """Get all bucket keys "near" this key. This method may return a generator."""
        minx, innerx = divmod(key[0], 16)
        miny, innery = divmod(key[1], 16)
        minz, innerz = divmod(key[2], 16)
        minx = int(minx)
        miny = int(miny)
        minz = int(minz)
        maxx = minx + 1
        maxy = miny + 1
        maxz = minz + 1
        if innerx <= radius:
            minx -= 1
        if innery <= radius:
            miny -= 1
        if innerz <= radius:
            minz -= 1
        if innerx + radius >= 16:
            maxx += 1
        if innery + radius >= 16:
            maxy += 1
        if innerz + radius >= 16:
            maxz += 1
        expand = int(radius // 16)
        minx -= expand
        miny -= expand
        minz -= expand
        maxx += expand
        maxy += expand
        maxz += expand
        return product(xrange(minx, maxx), xrange(miny, maxy), xrange(minz, maxz))


Prompt:
Implement the Python class `HfDistillerTrainer` described below.

Class description:
Hugging Face distillation-based trainer.

Method signatures and docstrings:
- def __init__(self, teacher_model: torch.nn.Module, **kwargs) -> None: Initialize Hugging Face distillation-based trainer. Args: teacher_model: Pre-trained teacher model.
- def compute_loss(self, model: torch.nn.Module, inputs: Dict[str, torch.Tensor], return_outputs: Optional[bool]=False) -> Tuple[torch.Tensor, ...]: Override the computation of the loss function. The loss is a weighted sum of the student's loss, as computed by the original `HfTrainer`, and the KL divergence between the student and teacher models. Args: model: Student model. inputs: Input tensors. return_outputs: Whether outputs should be returned. Returns: (loss, outputs) or the loss tensor.

Implement the Python class `HfDistillerTrainer` described below.

Class description:
Hugging Face distillation-based trainer.

Method signatures and docstrings:
- def __init__(self, teacher_model: torch.nn.Module, **kwargs) -> None: Initialize Hugging Face distillation-based trainer. Args: teacher_model: Pre-trained teacher model.
- def compute_loss(self, model: torch.nn.Module, inputs: Dict[str, torch.Tensor], return_outputs: Optional[bool]=False) -> Tuple[torch.Tensor, ...]: Override the computation of the loss function. The loss is a weighted sum of the student's loss, as computed by the original `HfTrainer`, and the KL divergence between the student and teacher models. Args: model: Student model. inputs: Input tensors. return_outputs: Whether outputs should be returned. Returns: (loss, outputs) or the loss tensor.

<|skeleton|>
class HfDistillerTrainer:
    """Hugging Face distillation-based trainer."""

    def __init__(self, teacher_model: torch.nn.Module, **kwargs) -> None:
        """Initialize Hugging Face distillation-based trainer. Args: teacher_model: Pre-trained teacher model."""
        <|body_0|>

    def compute_loss(self, model: torch.nn.Module, inputs: Dict[str, torch.Tensor], return_outputs: Optional[bool]=False) -> Tuple[torch.Tensor, ...]:
        """Override the computation of the loss function. The loss is a weighted sum of the student's loss, as computed by the original `HfTrainer`, and the KL divergence between the student and teacher models. Args: model: Student model. inputs: Input tensors. return_outputs: Whether outputs should be returned. Returns: (loss, outputs) or the loss tensor."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.teacher_model = teacher_model
        if 'args' in kwargs:
            assert isinstance(kwargs['args'], DistillerTrainingArguments), '`args` should be an instance of `DistillerTrainingArguments`.'
        else:
            kwargs['args'] = DistillerTrainingArguments('tmp')
        super().__init__(**kwargs)
<|end_body_0|>

<|body_start_1|>
        student_outputs = model(**inputs)
        student_loss = student_outputs['loss']
        student_logits = student_outputs['logits']
        with torch.no_grad():
            teacher_outputs = self.teacher_model(**inputs)
            teacher_logits = teacher_outputs['logits']
        kl_loss = nn.KLDivLoss(reduction='batchmean')
        kl_divergence = kl_loss(F.log_softmax(student_logits / self.args.temperature, dim=-1), F.softmax(teacher_logits / self.args.temperature, dim=-1))
        kd_loss = self.args.temperature ** 2 * kl_divergence
        loss = self.args.alpha * student_loss + (1 - self.args.alpha) * kd_loss
        return (loss, student_outputs) if return_outputs else loss
<|end_body_1|>

Solution:
class HfDistillerTrainer:
    """Hugging Face distillation-based trainer."""

    def __init__(self, teacher_model: torch.nn.Module, **kwargs) -> None:
        """Initialize Hugging Face distillation-based trainer. Args: teacher_model: Pre-trained teacher model."""
        self.teacher_model = teacher_model
        if 'args' in kwargs:
            assert isinstance(kwargs['args'], DistillerTrainingArguments), '`args` should be an instance of `DistillerTrainingArguments`.'
        else:
            kwargs['args'] = DistillerTrainingArguments('tmp')
        super().__init__(**kwargs)

    def compute_loss(self, model: torch.nn.Module, inputs: Dict[str, torch.Tensor], return_outputs: Optional[bool]=False) -> Tuple[torch.Tensor, ...]:
        """Override the computation of the loss function. The loss is a weighted sum of the student's loss, as computed by the original `HfTrainer`, and the KL divergence between the student and teacher models. Args: model: Student model. inputs: Input tensors. return_outputs: Whether outputs should be returned. Returns: (loss, outputs) or the loss tensor."""
        student_outputs = model(**inputs)
        student_loss = student_outputs['loss']
        student_logits = student_outputs['logits']
        with torch.no_grad():
            teacher_outputs = self.teacher_model(**inputs)
            teacher_logits = teacher_outputs['logits']
        kl_loss = nn.KLDivLoss(reduction='batchmean')
        kl_divergence = kl_loss(F.log_softmax(student_logits / self.args.temperature, dim=-1), F.softmax(teacher_logits / self.args.temperature, dim=-1))
        kd_loss = self.args.temperature ** 2 * kl_divergence
        loss = self.args.alpha * student_loss + (1 - self.args.alpha) * kd_loss
        return (loss, student_outputs) if return_outputs else loss


Prompt:
Implement the Python class `BulkInstallAppTaskInfo` described below.

Class description:
Implementation of the 'BulkInstallAppTaskInfo' model. Parameters for a bulk install app task. Attributes: job_id (string): Job id of the task. num_machines_failed (int): Number of machines on which task is started. num_machines_passed (int): Number of machines on which task is started. num_machines_total (int): Number of machines on which task is started. registering_app (RegisteringAppEnum): Application being registered. This param is used to indicate the app for which the job is created. 'oracle' indicates that the job was created for oracle app. 'msSql' indicates that the job was created for msSql app. 'physical' indicates that the job was created for physical machine. state (StateBulkIns

Method signatures and docstrings:
- def __init__(self, job_id=None, num_machines_failed=None, num_machines_passed=None, num_machines_total=None, registering_app=None, state=None): Constructor for the BulkInstallAppTaskInfo class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `BulkInstallAppTaskInfo` described below.

Class description:
Implementation of the 'BulkInstallAppTaskInfo' model. Parameters for a bulk install app task. Attributes: job_id (string): Job id of the task. num_machines_failed (int): Number of machines on which task is started. num_machines_passed (int): Number of machines on which task is started. num_machines_total (int): Number of machines on which task is started. registering_app (RegisteringAppEnum): Application being registered. This param is used to indicate the app for which the job is created. 'oracle' indicates that the job was created for oracle app. 'msSql' indicates that the job was created for msSql app. 'physical' indicates that the job was created for physical machine. state (StateBulkIns

Method signatures and docstrings:
- def __init__(self, job_id=None, num_machines_failed=None, num_machines_passed=None, num_machines_total=None, registering_app=None, state=None): Constructor for the BulkInstallAppTaskInfo class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class BulkInstallAppTaskInfo:
    """Implementation of the 'BulkInstallAppTaskInfo' model. Parameters for a bulk install app task. Attributes: job_id (string): Job id of the task. num_machines_failed (int): Number of machines on which task is started. num_machines_passed (int): Number of machines on which task is started. num_machines_total (int): Number of machines on which task is started. registering_app (RegisteringAppEnum): Application being registered. This param is used to indicate the app for which the job is created. 'oracle' indicates that the job was created for oracle app. 'msSql' indicates that the job was created for msSql app. 'physical' indicates that the job was created for physical machine. state (StateBulkIns"""

    def __init__(self, job_id=None, num_machines_failed=None, num_machines_passed=None, num_machines_total=None, registering_app=None, state=None):
        """Constructor for the BulkInstallAppTaskInfo class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.job_id = job_id
        self.num_machines_failed = num_machines_failed
        self.num_machines_passed = num_machines_passed
        self.num_machines_total = num_machines_total
        self.registering_app = registering_app
        self.state = state
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        job_id = dictionary.get('jobId')
        num_machines_failed = dictionary.get('numMachinesFailed')
        num_machines_passed = dictionary.get('numMachinesPassed')
        num_machines_total = dictionary.get('numMachinesTotal')
        registering_app = dictionary.get('registeringApp')
        state = dictionary.get('state')
        return cls(job_id, num_machines_failed, num_machines_passed, num_machines_total, registering_app, state)
<|end_body_1|>

Solution:
class BulkInstallAppTaskInfo:
    """Implementation of the 'BulkInstallAppTaskInfo' model. Parameters for a bulk install app task. Attributes: job_id (string): Job id of the task. num_machines_failed (int): Number of machines on which task is started. num_machines_passed (int): Number of machines on which task is started. num_machines_total (int): Number of machines on which task is started. registering_app (RegisteringAppEnum): Application being registered. This param is used to indicate the app for which the job is created. 'oracle' indicates that the job was created for oracle app. 'msSql' indicates that the job was created for msSql app. 'physical' indicates that the job was created for physical machine. state (StateBulkIns"""

    def __init__(self, job_id=None, num_machines_failed=None, num_machines_passed=None, num_machines_total=None, registering_app=None, state=None):
        """Constructor for the BulkInstallAppTaskInfo class"""
        self.job_id = job_id
        self.num_machines_failed = num_machines_failed
        self.num_machines_passed = num_machines_passed
        self.num_machines_total = num_machines_total
        self.registering_app = registering_app
        self.state = state

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        job_id = dictionary.get('jobId')
        num_machines_failed = dictionary.get('numMachinesFailed')
        num_machines_passed = dictionary.get('numMachinesPassed')
        num_machines_total = dictionary.get('numMachinesTotal')
        registering_app = dictionary.get('registeringApp')
        state = dictionary.get('state')
        return cls(job_id, num_machines_failed, num_machines_passed, num_machines_total, registering_app, state)