实现前向传播算法，减少计算图中节点的个数

evalute

定义损失函数来刻画预测值与真实值的差距

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

Class description:
神经网络

Method signatures and docstrings:
- def example_load_data(self): 加载数据
- def example_compute(self): 简单的神经网络实现前向传播的算法 这里为什么能用矩阵乘法来计算呢？因为，通过求加权和发现 正好是矩阵的乘法运算结果。所以，这里充分的体现了数学的美
- def example_2_load_data(self): 加载数据
- def example_2_compute(self): 实现前向传播算法，减少计算图中节点的个数
- def evalute(self): 定义损失函数来刻画预测值与真实值的差距

Implement the Python class `NeuralNetworks` described below.

Class description:
神经网络

Method signatures and docstrings:
- def example_load_data(self): 加载数据
- def example_compute(self): 简单的神经网络实现前向传播的算法 这里为什么能用矩阵乘法来计算呢？因为，通过求加权和发现 正好是矩阵的乘法运算结果。所以，这里充分的体现了数学的美
- def example_2_load_data(self): 加载数据
- def example_2_compute(self): 实现前向传播算法，减少计算图中节点的个数
- def evalute(self): 定义损失函数来刻画预测值与真实值的差距

<|skeleton|>
class NeuralNetworks:
    """神经网络"""

    def example_load_data(self):
        """加载数据"""
        <|body_0|>

    def example_compute(self):
        """简单的神经网络实现前向传播的算法 这里为什么能用矩阵乘法来计算呢？因为，通过求加权和发现 正好是矩阵的乘法运算结果。所以，这里充分的体现了数学的美"""
        <|body_1|>

    def example_2_load_data(self):
        """加载数据"""
        <|body_2|>

    def example_2_compute(self):
        """实现前向传播算法，减少计算图中节点的个数"""
        <|body_3|>

    def evalute(self):
        """定义损失函数来刻画预测值与真实值的差距"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.x = constant([[0.7, 0.9]])
        self.w1 = Variable(random_normal([2, 3], stddev=1, seed=1))
        self.w2 = Variable(random_normal([3, 1], stddev=1, seed=1))
<|end_body_0|>

<|body_start_1|>
        a = matmul(self.x, self.w1)
        y = matmul(a, self.w2)
        sess = Session()
        sess.run(self.w1.initializer)
        sess.run(self.w2.initializer)
        print('第一种：', sess.run(y))
        sess.close()
<|end_body_1|>

<|body_start_2|>
        self.w1 = Variable(random_normal([2, 3], stddev=1, seed=1))
        self.w2 = Variable(random_normal([3, 1], stddev=1, seed=1))
        self.x = placeholder(float32, shape=(3, 2), name='input')
<|end_body_2|>

<|body_start_3|>
        a = matmul(self.x, self.w1)
        y = matmul(a, self.w2)
        sess = Session()
        initOp = global_variables_initializer()
        sess.run(initOp)
        print('第二种：', sess.run(y, feed_dict={self.x: [[0.7, 0.9], [0.1, 0.4], [0.5, 0.8]]}))
        sess.close()
<|end_body_3|>

<|body_start_4|>
        """
                crossEntry = - reduce_mean(y * log(clip_by_value(y, 1e-10 1.0)))
                # 定义学习率
                learningRate = 0.001
                # 定义反向传播算法来优化神经网络中的参数
                trainStep = train.AdamOptimizer(learningRate).minimize(crossEntry)
                """
        pass
<|end_body_4|>

Solution:
class NeuralNetworks:
    """神经网络"""

    def example_load_data(self):
        """加载数据"""
        self.x = constant([[0.7, 0.9]])
        self.w1 = Variable(random_normal([2, 3], stddev=1, seed=1))
        self.w2 = Variable(random_normal([3, 1], stddev=1, seed=1))

    def example_compute(self):
        """简单的神经网络实现前向传播的算法 这里为什么能用矩阵乘法来计算呢？因为，通过求加权和发现 正好是矩阵的乘法运算结果。所以，这里充分的体现了数学的美"""
        a = matmul(self.x, self.w1)
        y = matmul(a, self.w2)
        sess = Session()
        sess.run(self.w1.initializer)
        sess.run(self.w2.initializer)
        print('第一种：', sess.run(y))
        sess.close()

    def example_2_load_data(self):
        """加载数据"""
        self.w1 = Variable(random_normal([2, 3], stddev=1, seed=1))
        self.w2 = Variable(random_normal([3, 1], stddev=1, seed=1))
        self.x = placeholder(float32, shape=(3, 2), name='input')

    def example_2_compute(self):
        """实现前向传播算法，减少计算图中节点的个数"""
        a = matmul(self.x, self.w1)
        y = matmul(a, self.w2)
        sess = Session()
        initOp = global_variables_initializer()
        sess.run(initOp)
        print('第二种：', sess.run(y, feed_dict={self.x: [[0.7, 0.9], [0.1, 0.4], [0.5, 0.8]]}))
        sess.close()

    def evalute(self):
        """定义损失函数来刻画预测值与真实值的差距"""
        """
                crossEntry = - reduce_mean(y * log(clip_by_value(y, 1e-10 1.0)))
                # 定义学习率
                learningRate = 0.001
                # 定义反向传播算法来优化神经网络中的参数
                trainStep = train.AdamOptimizer(learningRate).minimize(crossEntry)
                """
        pass


decide

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

Class description:
An implementation of Rank-Biased Precision, operationalised as a stopping strategy. Uses a stochastic roll of the dice to determine if a searcher continues or not. Implemented as per Moffat and Zobel (2008).

Method signatures and docstrings:
- def __init__(self, search_context, logger, patience=0.5, base_seed=0): Instantiates the decision maker, with a patience factor (defaulting to 0.5). The patience factor of RBP determines how patient a searcher is. The closer to 1.0, the deeper the searcher will go.
- def decide(self): Implements the basic RBP algorithm, using the RBP score computed with the roll of a dice to determine whether the searcher continues or not.

Implement the Python class `RBPDecisionMaker` described below.

Class description:
An implementation of Rank-Biased Precision, operationalised as a stopping strategy. Uses a stochastic roll of the dice to determine if a searcher continues or not. Implemented as per Moffat and Zobel (2008).

Method signatures and docstrings:
- def __init__(self, search_context, logger, patience=0.5, base_seed=0): Instantiates the decision maker, with a patience factor (defaulting to 0.5). The patience factor of RBP determines how patient a searcher is. The closer to 1.0, the deeper the searcher will go.
- def decide(self): Implements the basic RBP algorithm, using the RBP score computed with the roll of a dice to determine whether the searcher continues or not.

<|skeleton|>
class RBPDecisionMaker:
    """An implementation of Rank-Biased Precision, operationalised as a stopping strategy. Uses a stochastic roll of the dice to determine if a searcher continues or not. Implemented as per Moffat and Zobel (2008)."""

    def __init__(self, search_context, logger, patience=0.5, base_seed=0):
        """Instantiates the decision maker, with a patience factor (defaulting to 0.5). The patience factor of RBP determines how patient a searcher is. The closer to 1.0, the deeper the searcher will go."""
        <|body_0|>

    def decide(self):
        """Implements the basic RBP algorithm, using the RBP score computed with the roll of a dice to determine whether the searcher continues or not."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(RBPDecisionMaker, self).__init__(search_context, logger)
        self.__patience = patience
        self.__random = Random()
        self.__random.seed(base_seed + 1024)
<|end_body_0|>

<|body_start_1|>
        rank = self._search_context.get_current_serp_position()
        rbp_score = self.__patience ** (rank - 1)
        dp = self.__random.random()
        if dp > rbp_score:
            return Actions.QUERY
        return Actions.SNIPPET
<|end_body_1|>

Solution:
class RBPDecisionMaker:
    """An implementation of Rank-Biased Precision, operationalised as a stopping strategy. Uses a stochastic roll of the dice to determine if a searcher continues or not. Implemented as per Moffat and Zobel (2008)."""

    def __init__(self, search_context, logger, patience=0.5, base_seed=0):
        """Instantiates the decision maker, with a patience factor (defaulting to 0.5). The patience factor of RBP determines how patient a searcher is. The closer to 1.0, the deeper the searcher will go."""
        super(RBPDecisionMaker, self).__init__(search_context, logger)
        self.__patience = patience
        self.__random = Random()
        self.__random.seed(base_seed + 1024)

    def decide(self):
        """Implements the basic RBP algorithm, using the RBP score computed with the roll of a dice to determine whether the searcher continues or not."""
        rank = self._search_context.get_current_serp_position()
        rbp_score = self.__patience ** (rank - 1)
        dp = self.__random.random()
        if dp > rbp_score:
            return Actions.QUERY
        return Actions.SNIPPET


prune

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

Class description:
Specialized Manager for Container resources. Attributes: resource: Container subclass of PodmanResource, factory method will create these.

Method signatures and docstrings:
- def run(self, image: Union[str, Image], command: Union[str, List[str]]=None, stdout=True, stderr=False, remove: bool=False, **kwargs) -> Union[Container, Sequence[str]]: Run container. By default, run() will wait for the container to finish and return its logs. If detach=True, run() will start the container and return a Container object rather than logs. Args: image: Image to run. command: Command to run in the container. stdout: Include stdout. Default: True. stderr: Include stderr. Default: False. remove: Delete container when the container's processes exit. Default: False. Keyword Args: auto_remove (bool): Enable auto-removal of the container on daemon side when the container's process exits. blkio_weight_device (Dict[str, Any]): Block IO weight (relative device weight) in the form of: [{"Path": "device_path", "Weight": weight}]. blkio_weight (int): Block
- def create(self, image: Union[Image, str], command: Union[str, List[str]]=None, **kwargs) -> Container: Create a container. See Container.run() for arguments. The following are ignored: stdout, stderr, and remove. Raises: ImageNotFound: If given image does not exist. APIError: If service returns an error.
- def get(self, container_id: str) -> Container: Get container by name or id. Args: container_id: Container name or id. Raises: NotFound: Container does not exist. APIError: Error return by service.
- def list(self, **kwargs) -> List[Container]: Report on containers. Keyword Args: all: If False, only show running containers. Default: False. since: Show containers created after container name or id given. before: Show containers created before container name or id given. limit: Show last N created containers. filters: Filter container reported. Available filters: - exited (int): Only containers with specified exit code - status (str): One of restarting, running, paused, exited - label (Union[str, List[str]]): Format either "key", "key=value" or a list of such. - id (str): The id of the container. - name (str): The name of the container. - ancestor (str): Filter by container ancestor. Format of <image-name>[:tag], <image-id>, or <imag
- def prune(self, filters: Mapping[str, str]=None) -> Dict[str, Any]: Delete stopped containers. Args: filters: Dict of criteria for determining containers to remove. Available keys are: - until (str): Delete containers before this time - label (List[str]): Labels associated with containers Returns: List of deleted container id's and the freed disk space in bytes. Raises: APIError: If service reports an error

Implement the Python class `ContainersManager` described below.

Class description:
Specialized Manager for Container resources. Attributes: resource: Container subclass of PodmanResource, factory method will create these.

Method signatures and docstrings:
- def run(self, image: Union[str, Image], command: Union[str, List[str]]=None, stdout=True, stderr=False, remove: bool=False, **kwargs) -> Union[Container, Sequence[str]]: Run container. By default, run() will wait for the container to finish and return its logs. If detach=True, run() will start the container and return a Container object rather than logs. Args: image: Image to run. command: Command to run in the container. stdout: Include stdout. Default: True. stderr: Include stderr. Default: False. remove: Delete container when the container's processes exit. Default: False. Keyword Args: auto_remove (bool): Enable auto-removal of the container on daemon side when the container's process exits. blkio_weight_device (Dict[str, Any]): Block IO weight (relative device weight) in the form of: [{"Path": "device_path", "Weight": weight}]. blkio_weight (int): Block
- def create(self, image: Union[Image, str], command: Union[str, List[str]]=None, **kwargs) -> Container: Create a container. See Container.run() for arguments. The following are ignored: stdout, stderr, and remove. Raises: ImageNotFound: If given image does not exist. APIError: If service returns an error.
- def get(self, container_id: str) -> Container: Get container by name or id. Args: container_id: Container name or id. Raises: NotFound: Container does not exist. APIError: Error return by service.
- def list(self, **kwargs) -> List[Container]: Report on containers. Keyword Args: all: If False, only show running containers. Default: False. since: Show containers created after container name or id given. before: Show containers created before container name or id given. limit: Show last N created containers. filters: Filter container reported. Available filters: - exited (int): Only containers with specified exit code - status (str): One of restarting, running, paused, exited - label (Union[str, List[str]]): Format either "key", "key=value" or a list of such. - id (str): The id of the container. - name (str): The name of the container. - ancestor (str): Filter by container ancestor. Format of <image-name>[:tag], <image-id>, or <imag
- def prune(self, filters: Mapping[str, str]=None) -> Dict[str, Any]: Delete stopped containers. Args: filters: Dict of criteria for determining containers to remove. Available keys are: - until (str): Delete containers before this time - label (List[str]): Labels associated with containers Returns: List of deleted container id's and the freed disk space in bytes. Raises: APIError: If service reports an error

<|skeleton|>
class ContainersManager:
    """Specialized Manager for Container resources. Attributes: resource: Container subclass of PodmanResource, factory method will create these."""

    def run(self, image: Union[str, Image], command: Union[str, List[str]]=None, stdout=True, stderr=False, remove: bool=False, **kwargs) -> Union[Container, Sequence[str]]:
        """Run container. By default, run() will wait for the container to finish and return its logs. If detach=True, run() will start the container and return a Container object rather than logs. Args: image: Image to run. command: Command to run in the container. stdout: Include stdout. Default: True. stderr: Include stderr. Default: False. remove: Delete container when the container's processes exit. Default: False. Keyword Args: auto_remove (bool): Enable auto-removal of the container on daemon side when the container's process exits. blkio_weight_device (Dict[str, Any]): Block IO weight (relative device weight) in the form of: [{"Path": "device_path", "Weight": weight}]. blkio_weight (int): Block"""
        <|body_0|>

    def create(self, image: Union[Image, str], command: Union[str, List[str]]=None, **kwargs) -> Container:
        """Create a container. See Container.run() for arguments. The following are ignored: stdout, stderr, and remove. Raises: ImageNotFound: If given image does not exist. APIError: If service returns an error."""
        <|body_1|>

    def get(self, container_id: str) -> Container:
        """Get container by name or id. Args: container_id: Container name or id. Raises: NotFound: Container does not exist. APIError: Error return by service."""
        <|body_2|>

    def list(self, **kwargs) -> List[Container]:
        """Report on containers. Keyword Args: all: If False, only show running containers. Default: False. since: Show containers created after container name or id given. before: Show containers created before container name or id given. limit: Show last N created containers. filters: Filter container reported. Available filters: - exited (int): Only containers with specified exit code - status (str): One of restarting, running, paused, exited - label (Union[str, List[str]]): Format either "key", "key=value" or a list of such. - id (str): The id of the container. - name (str): The name of the container. - ancestor (str): Filter by container ancestor. Format of <image-name>[:tag], <image-id>, or <imag"""
        <|body_3|>

    def prune(self, filters: Mapping[str, str]=None) -> Dict[str, Any]:
        """Delete stopped containers. Args: filters: Dict of criteria for determining containers to remove. Available keys are: - until (str): Delete containers before this time - label (List[str]): Labels associated with containers Returns: List of deleted container id's and the freed disk space in bytes. Raises: APIError: If service reports an error"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        if isinstance(image, Image):
            image = image.id
        _ = command
        _ = stdout
        _ = stderr
        _ = remove
        _ = kwargs
        raise NotImplementedError
<|end_body_0|>

<|body_start_1|>
        if isinstance(image, Image):
            image = image.id
        _ = kwargs
        _ = command
        raise NotImplementedError
<|end_body_1|>

<|body_start_2|>
        container_id = urllib.parse.quote_plus(container_id)
        response = self.client.get(f'/containers/{container_id}/json')
        body = response.json()
        if response.status_code == 200:
            return self.prepare_model(body)
        if response.status_code == 404:
            raise NotFound(body['cause'], response=response, explanation=body['message'])
        raise APIError(body['cause'], response=response, explanation=body['message'])
<|end_body_2|>

<|body_start_3|>
        params = {'all': kwargs.get('all', None), 'filters': kwargs.get('filters', dict()), 'limit': kwargs.get('limit', None)}
        if 'before' in kwargs:
            params['filters']['before'] = kwargs.get('before')
        if 'since' in kwargs:
            params['filters']['since'] = kwargs.get('since')
        if len(params['filters']) > 0:
            params['filters'] = api.format_filters(params['filters'])
        response = self.client.get('/containers/json', params=params)
        body = response.json()
        if response.status_code != 200:
            raise APIError(body['cause'], response=response, explanation=body['message'])
        containers: List[Container] = []
        for element in body:
            containers.append(self.prepare_model(element))
        return containers
<|end_body_3|>

<|body_start_4|>
        params = dict()
        if filters is not None:
            params = {'filters', api.format_filters(filters)}
        response = self.client.post('/containers/prune', params=params)
        body = response.json()
        if response.status_code != 200:
            raise APIError(body['cause'], response=response, explanation=body['message'])
        results = {'ContainersDeleted': [], 'SpaceReclaimed': 0}
        for entry in body:
            if entry.get('error', None) is not None:
                raise APIError(entry['error'], response=response, explanation=entry['error'])
            results['ContainersDeleted'].append(entry['id'])
            results['SpaceReclaimed'] += entry['space']
        return results
<|end_body_4|>

Solution:
class ContainersManager:
    """Specialized Manager for Container resources. Attributes: resource: Container subclass of PodmanResource, factory method will create these."""

    def run(self, image: Union[str, Image], command: Union[str, List[str]]=None, stdout=True, stderr=False, remove: bool=False, **kwargs) -> Union[Container, Sequence[str]]:
        """Run container. By default, run() will wait for the container to finish and return its logs. If detach=True, run() will start the container and return a Container object rather than logs. Args: image: Image to run. command: Command to run in the container. stdout: Include stdout. Default: True. stderr: Include stderr. Default: False. remove: Delete container when the container's processes exit. Default: False. Keyword Args: auto_remove (bool): Enable auto-removal of the container on daemon side when the container's process exits. blkio_weight_device (Dict[str, Any]): Block IO weight (relative device weight) in the form of: [{"Path": "device_path", "Weight": weight}]. blkio_weight (int): Block"""
        if isinstance(image, Image):
            image = image.id
        _ = command
        _ = stdout
        _ = stderr
        _ = remove
        _ = kwargs
        raise NotImplementedError

    def create(self, image: Union[Image, str], command: Union[str, List[str]]=None, **kwargs) -> Container:
        """Create a container. See Container.run() for arguments. The following are ignored: stdout, stderr, and remove. Raises: ImageNotFound: If given image does not exist. APIError: If service returns an error."""
        if isinstance(image, Image):
            image = image.id
        _ = kwargs
        _ = command
        raise NotImplementedError

    def get(self, container_id: str) -> Container:
        """Get container by name or id. Args: container_id: Container name or id. Raises: NotFound: Container does not exist. APIError: Error return by service."""
        container_id = urllib.parse.quote_plus(container_id)
        response = self.client.get(f'/containers/{container_id}/json')
        body = response.json()
        if response.status_code == 200:
            return self.prepare_model(body)
        if response.status_code == 404:
            raise NotFound(body['cause'], response=response, explanation=body['message'])
        raise APIError(body['cause'], response=response, explanation=body['message'])

    def list(self, **kwargs) -> List[Container]:
        """Report on containers. Keyword Args: all: If False, only show running containers. Default: False. since: Show containers created after container name or id given. before: Show containers created before container name or id given. limit: Show last N created containers. filters: Filter container reported. Available filters: - exited (int): Only containers with specified exit code - status (str): One of restarting, running, paused, exited - label (Union[str, List[str]]): Format either "key", "key=value" or a list of such. - id (str): The id of the container. - name (str): The name of the container. - ancestor (str): Filter by container ancestor. Format of <image-name>[:tag], <image-id>, or <imag"""
        params = {'all': kwargs.get('all', None), 'filters': kwargs.get('filters', dict()), 'limit': kwargs.get('limit', None)}
        if 'before' in kwargs:
            params['filters']['before'] = kwargs.get('before')
        if 'since' in kwargs:
            params['filters']['since'] = kwargs.get('since')
        if len(params['filters']) > 0:
            params['filters'] = api.format_filters(params['filters'])
        response = self.client.get('/containers/json', params=params)
        body = response.json()
        if response.status_code != 200:
            raise APIError(body['cause'], response=response, explanation=body['message'])
        containers: List[Container] = []
        for element in body:
            containers.append(self.prepare_model(element))
        return containers

    def prune(self, filters: Mapping[str, str]=None) -> Dict[str, Any]:
        """Delete stopped containers. Args: filters: Dict of criteria for determining containers to remove. Available keys are: - until (str): Delete containers before this time - label (List[str]): Labels associated with containers Returns: List of deleted container id's and the freed disk space in bytes. Raises: APIError: If service reports an error"""
        params = dict()
        if filters is not None:
            params = {'filters', api.format_filters(filters)}
        response = self.client.post('/containers/prune', params=params)
        body = response.json()
        if response.status_code != 200:
            raise APIError(body['cause'], response=response, explanation=body['message'])
        results = {'ContainersDeleted': [], 'SpaceReclaimed': 0}
        for entry in body:
            if entry.get('error', None) is not None:
                raise APIError(entry['error'], response=response, explanation=entry['error'])
            results['ContainersDeleted'].append(entry['id'])
            results['SpaceReclaimed'] += entry['space']
        return results


permute

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def permute(self, nums): :type nums: List[int] :rtype: List[List[int]]

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def permute(self, nums): :type nums: List[int] :rtype: List[List[int]]

<|skeleton|>
class Solution:

    def nextPermutation(self, nums):
        """:type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
        <|body_0|>

    def permute(self, nums):
        """:type nums: List[int] :rtype: List[List[int]]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        tag1 = -1
        for i in range(len(nums) - 2, -1, -1):
            if nums[i] < nums[i + 1]:
                tag1 = i
                break
        if tag1 != -1:
            for i in range(len(nums) - 1, -1, -1):
                if nums[tag1] < nums[i]:
                    tmp = nums[tag1]
                    nums[tag1] = nums[i]
                    nums[i] = tmp
                    break
        else:
            return 1
        left, right = (tag1 + 1, len(nums) - 1)
        while left < right:
            tmp = nums[left]
            nums[left] = nums[right]
            nums[right] = tmp
            left = left + 1
            right = right - 1
<|end_body_0|>

<|body_start_1|>
        nums = sorted(nums)
        result = []
        while True:
            result.append(nums[:])
            if self.nextPermutation(nums):
                return result
<|end_body_1|>

Solution:
class Solution:
    def nextPermutation(self, nums):
        """:type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
        tag1 = -1
        for i in range(len(nums) - 2, -1, -1):
            if nums[i] < nums[i + 1]:
                tag1 = i
                break
        if tag1 != -1:
            for i in range(len(nums) - 1, -1, -1):
                if nums[tag1] < nums[i]:
                    tmp = nums[tag1]
                    nums[tag1] = nums[i]
                    nums[i] = tmp
                    break
        else:
            return 1
        left, right = (tag1 + 1, len(nums) - 1)
        while left < right:
            tmp = nums[left]
            nums[left] = nums[right]
            nums[right] = tmp
            left = left + 1
            right = right - 1

    def permute(self, nums):
        """:type nums: List[int] :rtype: List[List[int]]"""
        nums = sorted(nums)
        result = []
        while True:
            result.append(nums[:])
            if self.nextPermutation(nums):
                return result


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

Class description:
Test class that will test a board. Store the class to be tested in a board_class class variable.

Method signatures and docstrings:
- def test_str(self): Test that the magic string method on a full board works.
- def test_calc_winner_none(self): Test that calculating a winner returns None when no winner.
- def test_winner_won(self): Test that calculating a winner returns the winner.

Implement the Python class `BaseBoardTest` described below.

Class description:
Test class that will test a board. Store the class to be tested in a board_class class variable.

Method signatures and docstrings:
- def test_str(self): Test that the magic string method on a full board works.
- def test_calc_winner_none(self): Test that calculating a winner returns None when no winner.
- def test_winner_won(self): Test that calculating a winner returns the winner.

<|skeleton|>
class BaseBoardTest:
    """Test class that will test a board. Store the class to be tested in a board_class class variable."""

    def test_str(self):
        """Test that the magic string method on a full board works."""
        <|body_0|>

    def test_calc_winner_none(self):
        """Test that calculating a winner returns None when no winner."""
        <|body_1|>

    def test_winner_won(self):
        """Test that calculating a winner returns the winner."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        assert str(board) == 'O|X| \n |X| \n | | \n'
<|end_body_0|>

<|body_start_1|>
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        board.place_token(0, 2, 'O')
        assert board.calc_winner() is None
<|end_body_1|>

<|body_start_2|>
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        board.place_token(0, 2, 'O')
        board.place_token(1, 2, 'X')
        assert board.calc_winner() == 'X'
<|end_body_2|>

Solution:
class BaseBoardTest:
    """Test class that will test a board. Store the class to be tested in a board_class class variable."""

    def test_str(self):
        """Test that the magic string method on a full board works."""
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        assert str(board) == 'O|X| \n |X| \n | | \n'

    def test_calc_winner_none(self):
        """Test that calculating a winner returns None when no winner."""
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        board.place_token(0, 2, 'O')
        assert board.calc_winner() is None

    def test_winner_won(self):
        """Test that calculating a winner returns the winner."""
        board = self.board_class()
        board.place_token(1, 1, 'X')
        board.place_token(0, 0, 'O')
        board.place_token(1, 0, 'X')
        board.place_token(0, 2, 'O')
        board.place_token(1, 2, 'X')
        assert board.calc_winner() == 'X'


details

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

Class description:
Implement the OrderApi class.

Method signatures and docstrings:
- def details(self, request, *args, **kwargs): list user orders
- def create_order(self, request): create order takes address_id as query parameter to be inserted while creating the order

Implement the Python class `OrderApi` described below.

Class description:
Implement the OrderApi class.

Method signatures and docstrings:
- def details(self, request, *args, **kwargs): list user orders
- def create_order(self, request): create order takes address_id as query parameter to be inserted while creating the order

<|skeleton|>
class OrderApi:

    def details(self, request, *args, **kwargs):
        """list user orders"""
        <|body_0|>

    def create_order(self, request):
        """create order takes address_id as query parameter to be inserted while creating the order"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        user_id = request.user.id
        order = Order.objects.filter(user_id=user_id)
        serializer = OrderSerializer(order, many=True)
        return Response({'result': serializer.data, 'message': 'Done', 'status': True}, status=status.HTTP_200_OK)
<|end_body_0|>

<|body_start_1|>
        data = request.data
        address_id = request.query_params['address_id']
        data['user'] = request.user.id
        data['address'] = address_id
        serializer = OrderCreateSerializer(data=data)
        if serializer.is_valid():
            serializer.save()
            return Response({'result': serializer.data, 'message': 'Done', 'status': True}, status=status.HTTP_201_CREATED)
        return Response({'result': serializer.errors, 'message': 'Done', 'status': False}, status=status.HTTP_400_BAD_REQUEST)
<|end_body_1|>

Solution:
class OrderApi:
    def details(self, request, *args, **kwargs):
        """list user orders"""
        user_id = request.user.id
        order = Order.objects.filter(user_id=user_id)
        serializer = OrderSerializer(order, many=True)
        return Response({'result': serializer.data, 'message': 'Done', 'status': True}, status=status.HTTP_200_OK)

    def create_order(self, request):
        """create order takes address_id as query parameter to be inserted while creating the order"""
        data = request.data
        address_id = request.query_params['address_id']
        data['user'] = request.user.id
        data['address'] = address_id
        serializer = OrderCreateSerializer(data=data)
        if serializer.is_valid():
            serializer.save()
            return Response({'result': serializer.data, 'message': 'Done', 'status': True}, status=status.HTTP_201_CREATED)
        return Response({'result': serializer.errors, 'message': 'Done', 'status': False}, status=status.HTTP_400_BAD_REQUEST)


loss

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

Class description:
首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。

Method signatures and docstrings:
- def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001): 我们把需要学习的参数(W,B)都存在self.params字典中， 其中每个元素都是numpy array:
- def loss(self, X, y): 首先，输入的数据X是一个多维的array,shape为(样本图片的个数N * 32*32*3), y是与输入数据X对应的正确标签，shape为(N,)。 我们的loss函数目标输出一个损失值loss和一个grads字典， 其中存有loss关于隐层和输出层的参数(W,B)的梯度值：

Implement the Python class `TwoLayerNet` described below.

Class description:
首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。

Method signatures and docstrings:
- def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001): 我们把需要学习的参数(W,B)都存在self.params字典中， 其中每个元素都是numpy array:
- def loss(self, X, y): 首先，输入的数据X是一个多维的array,shape为(样本图片的个数N * 32*32*3), y是与输入数据X对应的正确标签，shape为(N,)。 我们的loss函数目标输出一个损失值loss和一个grads字典， 其中存有loss关于隐层和输出层的参数(W,B)的梯度值：

<|skeleton|>
class TwoLayerNet:
    """首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。"""

    def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001):
        """我们把需要学习的参数(W,B)都存在self.params字典中， 其中每个元素都是numpy array:"""
        <|body_0|>

    def loss(self, X, y):
        """首先，输入的数据X是一个多维的array,shape为(样本图片的个数N * 32*32*3), y是与输入数据X对应的正确标签，shape为(N,)。 我们的loss函数目标输出一个损失值loss和一个grads字典， 其中存有loss关于隐层和输出层的参数(W,B)的梯度值："""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.params = {}
        '\n        我们用标准差为weight_scale的高斯分布初始化参数W,\n        偏置B的初始化都为0：\n        (其中randn函数是基于零均值和标准差的一个高斯分布)\n        '
        self.params['W1'] = weight_scale * np.random.randn(input_dims, hidden_dims)
        self.params['b1'] = np.zeros((hidden_dims,))
        self.params['W2'] = weight_scale * np.random.randn(hidden_dims, num_classes)
        self.params['b2'] = np.zeros((num_classes,))
        '\n        可以看到，\n        隐藏层的参数矩阵行数是3*32*32(即上一层输入层的神经元个数),列数是100，\n        列数是自己定的没有什么依据；\n        输出层的参数矩阵行数是100(即上一层隐藏层的神经元个数),列数为10，即种类\n        '
<|end_body_0|>

<|body_start_1|>
        loss, grads = (0, {})
        h1_out, h1_cache = affine_relu_forward(X, self.params['W1'], self.params['b1'])
        scores, out_cache = affine_forward(h1_out, self.params['W2'], self.params['b2'])
        loss, dout = softmax_loss(scores, y)
        dout, dw2, db2 = affine_backward(dout, out_cache)
        grads['W2'] = dw2, grads['b2'] = db2
        _, dw1, db1 = affine_relu_backward(dout, h1_cache)
        grads['W1'], grads['b1'] = (dw1, db1)
        '\n        可以看到图片样本的数据梯度dout只起到了带路的作用,\n        最终会被舍弃掉，我们只要loss关于参数的梯度，\n        然后保存在字典里。\n        '
        return (loss, grads)
<|end_body_1|>

Solution:
class TwoLayerNet:
    """首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。"""

    def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001):
        """我们把需要学习的参数(W,B)都存在self.params字典中， 其中每个元素都是numpy array:"""
        self.params = {}
        '\n        我们用标准差为weight_scale的高斯分布初始化参数W,\n        偏置B的初始化都为0：\n        (其中randn函数是基于零均值和标准差的一个高斯分布)\n        '
        self.params['W1'] = weight_scale * np.random.randn(input_dims, hidden_dims)
        self.params['b1'] = np.zeros((hidden_dims,))
        self.params['W2'] = weight_scale * np.random.randn(hidden_dims, num_classes)
        self.params['b2'] = np.zeros((num_classes,))
        '\n        可以看到，\n        隐藏层的参数矩阵行数是3*32*32(即上一层输入层的神经元个数),列数是100，\n        列数是自己定的没有什么依据；\n        输出层的参数矩阵行数是100(即上一层隐藏层的神经元个数),列数为10，即种类\n        '

    def loss(self, X, y):
        """首先，输入的数据X是一个多维的array,shape为(样本图片的个数N * 32*32*3), y是与输入数据X对应的正确标签，shape为(N,)。 我们的loss函数目标输出一个损失值loss和一个grads字典， 其中存有loss关于隐层和输出层的参数(W,B)的梯度值："""
        loss, grads = (0, {})
        h1_out, h1_cache = affine_relu_forward(X, self.params['W1'], self.params['b1'])
        scores, out_cache = affine_forward(h1_out, self.params['W2'], self.params['b2'])
        loss, dout = softmax_loss(scores, y)
        dout, dw2, db2 = affine_backward(dout, out_cache)
        grads['W2'] = dw2, grads['b2'] = db2
        _, dw1, db1 = affine_relu_backward(dout, h1_cache)
        grads['W1'], grads['b1'] = (dw1, db1)
        '\n        可以看到图片样本的数据梯度dout只起到了带路的作用,\n        最终会被舍弃掉，我们只要loss关于参数的梯度，\n        然后保存在字典里。\n        '
        return (loss, grads)


put

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

Class description:
Implement the LRUCache class.

Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None

Implement the Python class `LRUCache` described below.

Class description:
Implement the LRUCache class.

Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None

<|skeleton|>
class LRUCache:

    def __init__(self, capacity):
        """:type capacity: int"""
        <|body_0|>

    def get(self, key):
        """:type key: int :rtype: int"""
        <|body_1|>

    def put(self, key, value):
        """:type key: int :type value: int :rtype: None"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.capacity = capacity
        self.queue = deque()
        self.items = {}
<|end_body_0|>

<|body_start_1|>
        if key in self.items:
            self.queue.remove(key)
            self.queue.appendleft(key)
            return self.items[key]
        else:
            return -1
<|end_body_1|>

<|body_start_2|>
        if key in self.items:
            self.queue.remove(key)
        elif len(self.queue) == self.capacity:
            del self.items[self.queue[-1]]
            self.queue.pop()
        self.queue.appendleft(key)
        self.items[key] = value
<|end_body_2|>

Solution:
class LRUCache:
    def __init__(self, capacity):
        """:type capacity: int"""
        self.capacity = capacity
        self.queue = deque()
        self.items = {}

    def get(self, key):
        """:type key: int :rtype: int"""
        if key in self.items:
            self.queue.remove(key)
            self.queue.appendleft(key)
            return self.items[key]
        else:
            return -1

    def put(self, key, value):
        """:type key: int :type value: int :rtype: None"""
        if key in self.items:
            self.queue.remove(key)
        elif len(self.queue) == self.capacity:
            del self.items[self.queue[-1]]
            self.queue.pop()
        self.queue.appendleft(key)
        self.items[key] = value


instance

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

Class description:
Load TurboJPEG only once. Ensures we do not log load failures each snapshot since camera image fetches happen every few seconds.

Method signatures and docstrings:
- def instance() -> TurboJPEG | Literal[False] | None: Singleton for TurboJPEG.
- def __init__(self) -> None: Try to create TurboJPEG only once.

Implement the Python class `TurboJPEGSingleton` described below.

Class description:
Load TurboJPEG only once. Ensures we do not log load failures each snapshot since camera image fetches happen every few seconds.

Method signatures and docstrings:
- def instance() -> TurboJPEG | Literal[False] | None: Singleton for TurboJPEG.
- def __init__(self) -> None: Try to create TurboJPEG only once.

<|skeleton|>
class TurboJPEGSingleton:
    """Load TurboJPEG only once. Ensures we do not log load failures each snapshot since camera image fetches happen every few seconds."""

    def instance() -> TurboJPEG | Literal[False] | None:
        """Singleton for TurboJPEG."""
        <|body_0|>

    def __init__(self) -> None:
        """Try to create TurboJPEG only once."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if TurboJPEGSingleton.__instance is None:
            TurboJPEGSingleton()
        return TurboJPEGSingleton.__instance
<|end_body_0|>

<|body_start_1|>
        try:
            from turbojpeg import TurboJPEG
            TurboJPEGSingleton.__instance = TurboJPEG()
        except Exception:
            _LOGGER.exception('Error loading libturbojpeg; Cameras may impact HomeKit performance')
            TurboJPEGSingleton.__instance = False
<|end_body_1|>

Solution:
class TurboJPEGSingleton:
    """Load TurboJPEG only once. Ensures we do not log load failures each snapshot since camera image fetches happen every few seconds."""

    def instance() -> TurboJPEG | Literal[False] | None:
        """Singleton for TurboJPEG."""
        if TurboJPEGSingleton.__instance is None:
            TurboJPEGSingleton()
        return TurboJPEGSingleton.__instance

    def __init__(self) -> None:
        """Try to create TurboJPEG only once."""
        try:
            from turbojpeg import TurboJPEG
            TurboJPEGSingleton.__instance = TurboJPEG()
        except Exception:
            _LOGGER.exception('Error loading libturbojpeg; Cameras may impact HomeKit performance')
            TurboJPEGSingleton.__instance = False


setUp

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

Class description:
Module tests for the water-regulation module

Method signatures and docstrings:
- def setUp(self): Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method.
- def test_run_water_regulator1(self): Run the sensor, pump, and controller against random real-life situations.
- def test_run_water_regulator2(self): Run the sensor, pump, and controller against random real-life situations, now with a changed decider.

Implement the Python class `ModuleTests` described below.

Class description:
Module tests for the water-regulation module

Method signatures and docstrings:
- def setUp(self): Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method.
- def test_run_water_regulator1(self): Run the sensor, pump, and controller against random real-life situations.
- def test_run_water_regulator2(self): Run the sensor, pump, and controller against random real-life situations, now with a changed decider.

<|skeleton|>
class ModuleTests:
    """Module tests for the water-regulation module"""

    def setUp(self):
        """Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method."""
        <|body_0|>

    def test_run_water_regulator1(self):
        """Run the sensor, pump, and controller against random real-life situations."""
        <|body_1|>

    def test_run_water_regulator2(self):
        """Run the sensor, pump, and controller against random real-life situations, now with a changed decider."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.sensor = Sensor('127.0.0.1', 8000)
        self.pump = P('127.0.0.1', 8000)
        self.pump.set_state = MagicMock(return_value=True)
<|end_body_0|>

<|body_start_1|>
        controller = Controller(self.sensor, self.pump, Decider(200, 0.1))
        self.sensor.measure = MagicMock(return_value=250)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OUT'])
        self.sensor.measure = MagicMock(return_value=215)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OUT)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OUT'])
        self.sensor.measure = MagicMock(return_value=199)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OUT)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])
<|end_body_1|>

<|body_start_2|>
        controller = Controller(self.sensor, self.pump, Decider(400, 0.08))
        self.sensor.measure = MagicMock(return_value=199)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_IN'])
        self.sensor.measure = MagicMock(return_value=380)
        self.pump.get_state = MagicMock(return_value=P.PUMP_IN)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_IN'])
        self.sensor.measure = MagicMock(return_value=401)
        self.pump.get_state = MagicMock(return_value=P.PUMP_IN)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])
        self.sensor.measure = MagicMock(return_value=401)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])
<|end_body_2|>

Solution:
class ModuleTests:
    """Module tests for the water-regulation module"""

    def setUp(self):
        """Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method."""
        self.sensor = Sensor('127.0.0.1', 8000)
        self.pump = P('127.0.0.1', 8000)
        self.pump.set_state = MagicMock(return_value=True)

    def test_run_water_regulator1(self):
        """Run the sensor, pump, and controller against random real-life situations."""
        controller = Controller(self.sensor, self.pump, Decider(200, 0.1))
        self.sensor.measure = MagicMock(return_value=250)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OUT'])
        self.sensor.measure = MagicMock(return_value=215)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OUT)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OUT'])
        self.sensor.measure = MagicMock(return_value=199)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OUT)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])

    def test_run_water_regulator2(self):
        """Run the sensor, pump, and controller against random real-life situations, now with a changed decider."""
        controller = Controller(self.sensor, self.pump, Decider(400, 0.08))
        self.sensor.measure = MagicMock(return_value=199)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_IN'])
        self.sensor.measure = MagicMock(return_value=380)
        self.pump.get_state = MagicMock(return_value=P.PUMP_IN)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_IN'])
        self.sensor.measure = MagicMock(return_value=401)
        self.pump.get_state = MagicMock(return_value=P.PUMP_IN)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])
        self.sensor.measure = MagicMock(return_value=401)
        self.pump.get_state = MagicMock(return_value=P.PUMP_OFF)
        controller.tick()
        self.pump.set_state.assert_called_with(controller.actions['PUMP_OFF'])


初始url请求返回reponse给解析函数

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

Class description:
Implement the XicispiderSpider class.

Method signatures and docstrings:
- def start_requests(self): 初始url请求返回reponse给解析函数
- def parse(self, response): 解析每一个url返回的rasponse

Implement the Python class `XicispiderSpider` described below.

Class description:
Implement the XicispiderSpider class.

Method signatures and docstrings:
- def start_requests(self): 初始url请求返回reponse给解析函数
- def parse(self, response): 解析每一个url返回的rasponse

<|skeleton|>
class XicispiderSpider:

    def start_requests(self):
        """初始url请求返回reponse给解析函数"""
        <|body_0|>

    def parse(self, response):
        """解析每一个url返回的rasponse"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for i in range(1, 664):
            url = 'http://www.xicidaili.com/nt/' + str(i)
            yield Request(url, headers=self.header, callback=self.parse)
<|end_body_0|>

<|body_start_1|>
        ip_list = response.xpath('//table[@id="ip_list"]/tr')
        pre_item = XiciItem()
        for ip in ip_list[1:]:
            pre_item['ip'] = ip.xpath('td[2]/text()')[0].extract()
            pre_item['port'] = ip.xpath('td[3]/text()')[0].extract()
            pre_item['speed'] = ip.xpath('td[7]/div/@title')[0].extract()
            pre_item['site'] = ip.xpath('string(td[4]/a/text())').extract()[0].strip()
            pre_item['surtime'] = ip.xpath('td[9]/text()')[0].extract()
            print(pre_item)
        return pre_item
<|end_body_1|>

Solution:
class XicispiderSpider:
    def start_requests(self):
        """初始url请求返回reponse给解析函数"""
        for i in range(1, 664):
            url = 'http://www.xicidaili.com/nt/' + str(i)
            yield Request(url, headers=self.header, callback=self.parse)

    def parse(self, response):
        """解析每一个url返回的rasponse"""
        ip_list = response.xpath('//table[@id="ip_list"]/tr')
        pre_item = XiciItem()
        for ip in ip_list[1:]:
            pre_item['ip'] = ip.xpath('td[2]/text()')[0].extract()
            pre_item['port'] = ip.xpath('td[3]/text()')[0].extract()
            pre_item['speed'] = ip.xpath('td[7]/div/@title')[0].extract()
            pre_item['site'] = ip.xpath('string(td[4]/a/text())').extract()[0].strip()
            pre_item['surtime'] = ip.xpath('td[9]/text()')[0].extract()
            print(pre_item)
        return pre_item


delete

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

Class description:
Class for specific meetup

Method signatures and docstrings:
- def get(self, meetupId, current_user): Method to fetch specific meetup
- def delete(self, meetupId, current_user): Method to delete meetup record

Implement the Python class `OneMeetup` described below.

Class description:
Class for specific meetup

Method signatures and docstrings:
- def get(self, meetupId, current_user): Method to fetch specific meetup
- def delete(self, meetupId, current_user): Method to delete meetup record

<|skeleton|>
class OneMeetup:
    """Class for specific meetup"""

    def get(self, meetupId, current_user):
        """Method to fetch specific meetup"""
        <|body_0|>

    def delete(self, meetupId, current_user):
        """Method to delete meetup record"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        meetup = MeetupModels.get_specific(self, meetupId)
        meetup = json.loads(meetup)
        if not meetup:
            return ({'status': 404, 'error': 'Meetup {} does not exist'.format(meetupId)}, 404)
        return ({'status': 200, 'meetups': meetup}, 200)
<|end_body_0|>

<|body_start_1|>
        delete_meetup = MeetupModels.delete_specific(self, meetupId)
        if delete_meetup is False:
            return ({'status': 404, 'error': 'Meetup {} does not exist'.format(meetupId)}, 404)
        return ({'status': 200, 'data': 'Meetup {} deleted'.format(meetupId)}, 200)
<|end_body_1|>

Solution:
class OneMeetup:
    """Class for specific meetup"""

    def get(self, meetupId, current_user):
        """Method to fetch specific meetup"""
        meetup = MeetupModels.get_specific(self, meetupId)
        meetup = json.loads(meetup)
        if not meetup:
            return ({'status': 404, 'error': 'Meetup {} does not exist'.format(meetupId)}, 404)
        return ({'status': 200, 'meetups': meetup}, 200)

    def delete(self, meetupId, current_user):
        """Method to delete meetup record"""
        delete_meetup = MeetupModels.delete_specific(self, meetupId)
        if delete_meetup is False:
            return ({'status': 404, 'error': 'Meetup {} does not exist'.format(meetupId)}, 404)
        return ({'status': 200, 'data': 'Meetup {} deleted'.format(meetupId)}, 200)


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

Class description:
YaraScanner class.

Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None): Initialize YaraEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None
- def scan_file(self, file_path): Scan file using Yara rules. Args: file_path (str): Path of the file to scan Raises: None Returns: None

Implement the Python class `YaraScanner` described below.

Class description:
YaraScanner class.

Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None): Initialize YaraEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None
- def scan_file(self, file_path): Scan file using Yara rules. Args: file_path (str): Path of the file to scan Raises: None Returns: None

<|skeleton|>
class YaraScanner:
    """YaraScanner class."""

    def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None):
        """Initialize YaraEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None"""
        <|body_0|>

    def scan_file(self, file_path):
        """Scan file using Yara rules. Args: file_path (str): Path of the file to scan Raises: None Returns: None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(debug, config_path, file_list, vt_api_key)
        if self.os_name:
            try:
                self._WORKERS = self.config_dict[self.os_name]['scanner']['yara']['threads']
                self._YARA_STORAGE = self.config_dict[self.os_name]['update']['yara']['storage']
            except KeyError:
                self.logger.log('Could not load configuration for: {}'.format(self.os_name), logtype='error')
                sys.exit(0)
        else:
            self.logger.log('Could not determine the OS', logtype='error')
            sys.exit(0)
<|end_body_0|>

<|body_start_1|>
        if yara_status:
            yara_files_list = os.listdir(self._YARA_STORAGE)
            for yara_file in yara_files_list:
                if yara_file.endswith('.yar') or yara_file.endswith('.yara'):
                    yara_file_path = os.path.join(self._YARA_STORAGE, yara_file)
                    rule_compile = yara.compile(yara_file_path)
                    matches = rule_compile.match(file_path)
                    if matches:
                        self.logger.log('Possible malicious file detected: {0}'.format(file_path), logtype='warning')
                        if file_path not in self.malicious_file_list:
                            self.malicious_file_list.append(file_path)
                            super().check_virus_total(file_path)
                            return
                        return
<|end_body_1|>

Solution:
class YaraScanner:
    """YaraScanner class."""

    def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None):
        """Initialize YaraEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None"""
        super().__init__(debug, config_path, file_list, vt_api_key)
        if self.os_name:
            try:
                self._WORKERS = self.config_dict[self.os_name]['scanner']['yara']['threads']
                self._YARA_STORAGE = self.config_dict[self.os_name]['update']['yara']['storage']
            except KeyError:
                self.logger.log('Could not load configuration for: {}'.format(self.os_name), logtype='error')
                sys.exit(0)
        else:
            self.logger.log('Could not determine the OS', logtype='error')
            sys.exit(0)

    def scan_file(self, file_path):
        """Scan file using Yara rules. Args: file_path (str): Path of the file to scan Raises: None Returns: None"""
        if yara_status:
            yara_files_list = os.listdir(self._YARA_STORAGE)
            for yara_file in yara_files_list:
                if yara_file.endswith('.yar') or yara_file.endswith('.yara'):
                    yara_file_path = os.path.join(self._YARA_STORAGE, yara_file)
                    rule_compile = yara.compile(yara_file_path)
                    matches = rule_compile.match(file_path)
                    if matches:
                        self.logger.log('Possible malicious file detected: {0}'.format(file_path), logtype='warning')
                        if file_path not in self.malicious_file_list:
                            self.malicious_file_list.append(file_path)
                            super().check_virus_total(file_path)
                            return
                        return


RunTrigger

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

Class description:
A Debian "install" trigger.

Method signatures and docstrings:
- def __init__(self, cache, package, service_manager): Constructor. Args: cache: An apt cache. package: A package to be installed. service_manager: A service manager for the application.
- def RunTrigger(self): Run a Debian "install" trigger. This trigger first records services present on the system, then installs the package under test, and finally records the newly installed services. Raises: triggers.TriggerError if the package cannot be installed.

Implement the Python class `DebInstall` described below.

Class description:
A Debian "install" trigger.

Method signatures and docstrings:
- def __init__(self, cache, package, service_manager): Constructor. Args: cache: An apt cache. package: A package to be installed. service_manager: A service manager for the application.
- def RunTrigger(self): Run a Debian "install" trigger. This trigger first records services present on the system, then installs the package under test, and finally records the newly installed services. Raises: triggers.TriggerError if the package cannot be installed.

<|skeleton|>
class DebInstall:
    """A Debian "install" trigger."""

    def __init__(self, cache, package, service_manager):
        """Constructor. Args: cache: An apt cache. package: A package to be installed. service_manager: A service manager for the application."""
        <|body_0|>

    def RunTrigger(self):
        """Run a Debian "install" trigger. This trigger first records services present on the system, then installs the package under test, and finally records the newly installed services. Raises: triggers.TriggerError if the package cannot be installed."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self._cache = cache
        self._package = package
        self._service_manager = service_manager
<|end_body_0|>

<|body_start_1|>
        self._service_manager.RecordServices()
        try:
            self._package.mark_install(True, True, False)
            logging.info('Installing...')
            self._cache.commit()
        except (apt.cache.FetchFailedException, SystemError) as e:
            logging.error('Could not install package %s: %s', self._package.name, e)
            raise triggers.TriggerError('Could not install package %s: %s' % (self._package.name, str(e)))
        self._service_manager.RecordNewServices()
<|end_body_1|>

Solution:
class DebInstall:
    """A Debian "install" trigger."""

    def __init__(self, cache, package, service_manager):
        """Constructor. Args: cache: An apt cache. package: A package to be installed. service_manager: A service manager for the application."""
        self._cache = cache
        self._package = package
        self._service_manager = service_manager

    def RunTrigger(self):
        """Run a Debian "install" trigger. This trigger first records services present on the system, then installs the package under test, and finally records the newly installed services. Raises: triggers.TriggerError if the package cannot be installed."""
        self._service_manager.RecordServices()
        try:
            self._package.mark_install(True, True, False)
            logging.info('Installing...')
            self._cache.commit()
        except (apt.cache.FetchFailedException, SystemError) as e:
            logging.error('Could not install package %s: %s', self._package.name, e)
            raise triggers.TriggerError('Could not install package %s: %s' % (self._package.name, str(e)))
        self._service_manager.RecordNewServices()


forward

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

Class description:
Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): number of heads n_feat (int): size of the features dropout_rate (float): dropout rate

Method signatures and docstrings:
- def __init__(self, n_head, n_feat, dropout_rate, pos_bias_u, pos_bias_v, max_cache_len=0): Construct an RelPositionMultiHeadedAttention object.
- def rel_shift(self, x): Compute relative positional encoding. Args: x (torch.Tensor): (batch, nheads, time, 2*time-1)
- def forward(self, query, key, value, mask, pos_emb, cache=None): Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): (batch, time1, size) key (torch.Tensor): (batch, time2, size) value(torch.Tensor): (batch, time2, size) mask (torch.Tensor): (batch, time1, time2) pos_emb (torch.Tensor) : (batch, time1, size) cache (torch.Tensor) : (batch, time_cache, size) Returns: output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention cache (torch.Tensor) : (batch, time_cache_next, size)

Implement the Python class `RelPositionMultiHeadAttention` described below.

Class description:
Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): number of heads n_feat (int): size of the features dropout_rate (float): dropout rate

Method signatures and docstrings:
- def __init__(self, n_head, n_feat, dropout_rate, pos_bias_u, pos_bias_v, max_cache_len=0): Construct an RelPositionMultiHeadedAttention object.
- def rel_shift(self, x): Compute relative positional encoding. Args: x (torch.Tensor): (batch, nheads, time, 2*time-1)
- def forward(self, query, key, value, mask, pos_emb, cache=None): Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): (batch, time1, size) key (torch.Tensor): (batch, time2, size) value(torch.Tensor): (batch, time2, size) mask (torch.Tensor): (batch, time1, time2) pos_emb (torch.Tensor) : (batch, time1, size) cache (torch.Tensor) : (batch, time_cache, size) Returns: output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention cache (torch.Tensor) : (batch, time_cache_next, size)

<|skeleton|>
class RelPositionMultiHeadAttention:
    """Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): number of heads n_feat (int): size of the features dropout_rate (float): dropout rate"""

    def __init__(self, n_head, n_feat, dropout_rate, pos_bias_u, pos_bias_v, max_cache_len=0):
        """Construct an RelPositionMultiHeadedAttention object."""
        <|body_0|>

    def rel_shift(self, x):
        """Compute relative positional encoding. Args: x (torch.Tensor): (batch, nheads, time, 2*time-1)"""
        <|body_1|>

    def forward(self, query, key, value, mask, pos_emb, cache=None):
        """Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): (batch, time1, size) key (torch.Tensor): (batch, time2, size) value(torch.Tensor): (batch, time2, size) mask (torch.Tensor): (batch, time1, time2) pos_emb (torch.Tensor) : (batch, time1, size) cache (torch.Tensor) : (batch, time_cache, size) Returns: output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention cache (torch.Tensor) : (batch, time_cache_next, size)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=max_cache_len)
        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
        if pos_bias_u is None or pos_bias_v is None:
            self.pos_bias_u = nn.Parameter(torch.FloatTensor(self.h, self.d_k))
            self.pos_bias_v = nn.Parameter(torch.FloatTensor(self.h, self.d_k))
            nn.init.zeros_(self.pos_bias_u)
            nn.init.zeros_(self.pos_bias_v)
        else:
            self.pos_bias_u = pos_bias_u
            self.pos_bias_v = pos_bias_v
<|end_body_0|>

<|body_start_1|>
        b, h, qlen, pos_len = x.size()
        x = torch.nn.functional.pad(x, pad=(1, 0))
        x = x.view(b, h, -1, qlen)
        x = x[:, :, 1:].view(b, h, qlen, pos_len)
        return x
<|end_body_1|>

<|body_start_2|>
        key, value, query, cache = self.update_cache(key=key, value=value, query=query, cache=cache)
        if torch.is_autocast_enabled():
            query, key, value = (query.to(torch.float32), key.to(torch.float32), value.to(torch.float32))
        with avoid_float16_autocast_context():
            q, k, v = self.forward_qkv(query, key, value)
            q = q.transpose(1, 2)
            n_batch_pos = pos_emb.size(0)
            p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
            p = p.transpose(1, 2)
            q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
            q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
            matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
            matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
            matrix_bd = self.rel_shift(matrix_bd)
            matrix_bd = matrix_bd[:, :, :, :matrix_ac.size(-1)]
            scores = (matrix_ac + matrix_bd) / self.s_d_k
            out = self.forward_attention(v, scores, mask)
        if cache is None:
            return out
        else:
            return (out, cache)
<|end_body_2|>

Solution:
class RelPositionMultiHeadAttention:
    """Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): number of heads n_feat (int): size of the features dropout_rate (float): dropout rate"""

    def __init__(self, n_head, n_feat, dropout_rate, pos_bias_u, pos_bias_v, max_cache_len=0):
        """Construct an RelPositionMultiHeadedAttention object."""
        super().__init__(n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=max_cache_len)
        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
        if pos_bias_u is None or pos_bias_v is None:
            self.pos_bias_u = nn.Parameter(torch.FloatTensor(self.h, self.d_k))
            self.pos_bias_v = nn.Parameter(torch.FloatTensor(self.h, self.d_k))
            nn.init.zeros_(self.pos_bias_u)
            nn.init.zeros_(self.pos_bias_v)
        else:
            self.pos_bias_u = pos_bias_u
            self.pos_bias_v = pos_bias_v

    def rel_shift(self, x):
        """Compute relative positional encoding. Args: x (torch.Tensor): (batch, nheads, time, 2*time-1)"""
        b, h, qlen, pos_len = x.size()
        x = torch.nn.functional.pad(x, pad=(1, 0))
        x = x.view(b, h, -1, qlen)
        x = x[:, :, 1:].view(b, h, qlen, pos_len)
        return x

    def forward(self, query, key, value, mask, pos_emb, cache=None):
        """Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): (batch, time1, size) key (torch.Tensor): (batch, time2, size) value(torch.Tensor): (batch, time2, size) mask (torch.Tensor): (batch, time1, time2) pos_emb (torch.Tensor) : (batch, time1, size) cache (torch.Tensor) : (batch, time_cache, size) Returns: output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention cache (torch.Tensor) : (batch, time_cache_next, size)"""
        key, value, query, cache = self.update_cache(key=key, value=value, query=query, cache=cache)
        if torch.is_autocast_enabled():
            query, key, value = (query.to(torch.float32), key.to(torch.float32), value.to(torch.float32))
        with avoid_float16_autocast_context():
            q, k, v = self.forward_qkv(query, key, value)
            q = q.transpose(1, 2)
            n_batch_pos = pos_emb.size(0)
            p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
            p = p.transpose(1, 2)
            q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
            q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
            matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
            matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
            matrix_bd = self.rel_shift(matrix_bd)
            matrix_bd = matrix_bd[:, :, :, :matrix_ac.size(-1)]
            scores = (matrix_ac + matrix_bd) / self.s_d_k
            out = self.forward_attention(v, scores, mask)
        if cache is None:
            return out
        else:
            return (out, cache)


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

Class description:
Internal class used to call some function of objects

Method signatures and docstrings:
- def __init__(self, name, decls): creates call_redirector_t instance. :param name: name of method, to be called on every object in the `decls` list :param decls: list of objects
- def __call__(self, *arguments, **keywords): calls method :attr:`call_redirector_t.name` on every object within the :attr:`call_redirector_t.decls` list

Implement the Python class `call_redirector_t` described below.

Class description:
Internal class used to call some function of objects

Method signatures and docstrings:
- def __init__(self, name, decls): creates call_redirector_t instance. :param name: name of method, to be called on every object in the `decls` list :param decls: list of objects
- def __call__(self, *arguments, **keywords): calls method :attr:`call_redirector_t.name` on every object within the :attr:`call_redirector_t.decls` list

<|skeleton|>
class call_redirector_t:
    """Internal class used to call some function of objects"""

    def __init__(self, name, decls):
        """creates call_redirector_t instance. :param name: name of method, to be called on every object in the `decls` list :param decls: list of objects"""
        <|body_0|>

    def __call__(self, *arguments, **keywords):
        """calls method :attr:`call_redirector_t.name` on every object within the :attr:`call_redirector_t.decls` list"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        object.__init__(self)
        self.name = name
        self.decls = decls
<|end_body_0|>

<|body_start_1|>
        for d in self.decls:
            callable_ = getattr(d, self.name)
            callable_(*arguments, **keywords)
<|end_body_1|>

Solution:
class call_redirector_t:
    """Internal class used to call some function of objects"""

    def __init__(self, name, decls):
        """creates call_redirector_t instance. :param name: name of method, to be called on every object in the `decls` list :param decls: list of objects"""
        object.__init__(self)
        self.name = name
        self.decls = decls

    def __call__(self, *arguments, **keywords):
        """calls method :attr:`call_redirector_t.name` on every object within the :attr:`call_redirector_t.decls` list"""
        for d in self.decls:
            callable_ = getattr(d, self.name)
            callable_(*arguments, **keywords)


rotate

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rotate(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.
- def rotate_2(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rotate(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.
- def rotate_2(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.

<|skeleton|>
class Solution:

    def rotate(self, matrix):
        """:type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
        <|body_0|>

    def rotate_2(self, matrix):
        """:type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        s = len(matrix)
        for i in range(s):
            for j in range(s - i):
                matrix[i][j], matrix[s - j - 1][s - i - 1] = (matrix[s - j - 1][s - i - 1], matrix[i][j])
        for i in range(s / 2):
            for j in range(s):
                matrix[i][j], matrix[s - 1 - i][j] = (matrix[s - 1 - i][j], matrix[i][j])
<|end_body_0|>

<|body_start_1|>
        lo = 0
        hi = len(matrix) - 1
        while lo < hi:
            for i in range(hi - lo):
                temp = matrix[lo][lo + i]
                matrix[lo][lo + i] = matrix[hi - i][lo]
                matrix[hi - i][lo] = matrix[hi][hi - i]
                matrix[hi][hi - i] = matrix[lo + i][hi]
                matrix[lo + i][hi] = temp
            lo += 1
            hi -= 1
<|end_body_1|>

Solution:
class Solution:
    def rotate(self, matrix):
        """:type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
        s = len(matrix)
        for i in range(s):
            for j in range(s - i):
                matrix[i][j], matrix[s - j - 1][s - i - 1] = (matrix[s - j - 1][s - i - 1], matrix[i][j])
        for i in range(s / 2):
            for j in range(s):
                matrix[i][j], matrix[s - 1 - i][j] = (matrix[s - 1 - i][j], matrix[i][j])

    def rotate_2(self, matrix):
        """:type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
        lo = 0
        hi = len(matrix) - 1
        while lo < hi:
            for i in range(hi - lo):
                temp = matrix[lo][lo + i]
                matrix[lo][lo + i] = matrix[hi - i][lo]
                matrix[hi - i][lo] = matrix[hi][hi - i]
                matrix[hi][hi - i] = matrix[lo + i][hi]
                matrix[lo + i][hi] = temp
            lo += 1
            hi -= 1


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

Class description:
Creates NetworkMemberGroup for a Network (Experience Site) for Profiles and Permission Sets that don't already have a corresponding NetworkMemberGroup. Raises exceptions if records cannot be found: - Network with Name network_name - Profiles with Names in profile_names - Permission Sets with Names in permission_set_names

Method signatures and docstrings:
- def _get_network_id(self, network_name: str) -> str: Returns Id of Network record with Name network_name. Raises a SalesforceException if no Network is found.
- def _get_network_member_group_parent_ids(self, network_id) -> set: Collect existing NetworkMemberGroup Parent IDs (associated Profile or Permission Set ID). An excpetion is thrown trying to create a NetworkMemberGroup for a parent who already has a record.
- def _get_parent_ids_by_name(self, sobject_type: str, record_names: List[str]): Returns a Dict: Name --> ID of records with Name in record_names for sObject_type. Dict value are None for all record_names that do not have corresponding records.
- def _process_parent(self, sobject_type, record_names) -> None: For a specific sobject_type and record_names, queries all Salesforce IDs corresponding to records of SObjectType sobject_type with Name in record_names. Then, tries to create NetworkMemberGroup for each parent in record_names.
- def _create_network_member_group(self, sobject_type, parent_name, parent_id) -> None: Processes and logs creating a NetworkMemberGroup for a specific parent. Outcomes: - Raises a CumulusCIException if record_id is None meaning no corresponding record was found in _get_parent_ids_by_name. - Logs a warning that a NetworkMemberGroup already exists is parent_id is in self._parent_ids. - Creates a NetworkMemberGroup for parent_id and logs the result.
- def _run_task(self): Gets required information then tries to create NetworkMemberGroups for Profiles and Permission Sets cooresponding to profile_names and permission_set_names respectively.

Implement the Python class `CreateNetworkMemberGroups` described below.

Class description:
Creates NetworkMemberGroup for a Network (Experience Site) for Profiles and Permission Sets that don't already have a corresponding NetworkMemberGroup. Raises exceptions if records cannot be found: - Network with Name network_name - Profiles with Names in profile_names - Permission Sets with Names in permission_set_names

Method signatures and docstrings:
- def _get_network_id(self, network_name: str) -> str: Returns Id of Network record with Name network_name. Raises a SalesforceException if no Network is found.
- def _get_network_member_group_parent_ids(self, network_id) -> set: Collect existing NetworkMemberGroup Parent IDs (associated Profile or Permission Set ID). An excpetion is thrown trying to create a NetworkMemberGroup for a parent who already has a record.
- def _get_parent_ids_by_name(self, sobject_type: str, record_names: List[str]): Returns a Dict: Name --> ID of records with Name in record_names for sObject_type. Dict value are None for all record_names that do not have corresponding records.
- def _process_parent(self, sobject_type, record_names) -> None: For a specific sobject_type and record_names, queries all Salesforce IDs corresponding to records of SObjectType sobject_type with Name in record_names. Then, tries to create NetworkMemberGroup for each parent in record_names.
- def _create_network_member_group(self, sobject_type, parent_name, parent_id) -> None: Processes and logs creating a NetworkMemberGroup for a specific parent. Outcomes: - Raises a CumulusCIException if record_id is None meaning no corresponding record was found in _get_parent_ids_by_name. - Logs a warning that a NetworkMemberGroup already exists is parent_id is in self._parent_ids. - Creates a NetworkMemberGroup for parent_id and logs the result.
- def _run_task(self): Gets required information then tries to create NetworkMemberGroups for Profiles and Permission Sets cooresponding to profile_names and permission_set_names respectively.

<|skeleton|>
class CreateNetworkMemberGroups:
    """Creates NetworkMemberGroup for a Network (Experience Site) for Profiles and Permission Sets that don't already have a corresponding NetworkMemberGroup. Raises exceptions if records cannot be found: - Network with Name network_name - Profiles with Names in profile_names - Permission Sets with Names in permission_set_names"""

    def _get_network_id(self, network_name: str) -> str:
        """Returns Id of Network record with Name network_name. Raises a SalesforceException if no Network is found."""
        <|body_0|>

    def _get_network_member_group_parent_ids(self, network_id) -> set:
        """Collect existing NetworkMemberGroup Parent IDs (associated Profile or Permission Set ID). An excpetion is thrown trying to create a NetworkMemberGroup for a parent who already has a record."""
        <|body_1|>

    def _get_parent_ids_by_name(self, sobject_type: str, record_names: List[str]):
        """Returns a Dict: Name --> ID of records with Name in record_names for sObject_type. Dict value are None for all record_names that do not have corresponding records."""
        <|body_2|>

    def _process_parent(self, sobject_type, record_names) -> None:
        """For a specific sobject_type and record_names, queries all Salesforce IDs corresponding to records of SObjectType sobject_type with Name in record_names. Then, tries to create NetworkMemberGroup for each parent in record_names."""
        <|body_3|>

    def _create_network_member_group(self, sobject_type, parent_name, parent_id) -> None:
        """Processes and logs creating a NetworkMemberGroup for a specific parent. Outcomes: - Raises a CumulusCIException if record_id is None meaning no corresponding record was found in _get_parent_ids_by_name. - Logs a warning that a NetworkMemberGroup already exists is parent_id is in self._parent_ids. - Creates a NetworkMemberGroup for parent_id and logs the result."""
        <|body_4|>

    def _run_task(self):
        """Gets required information then tries to create NetworkMemberGroups for Profiles and Permission Sets cooresponding to profile_names and permission_set_names respectively."""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        networks = self.sf.query_all(format_soql('SELECT Id FROM Network WHERE Name = {network_name} LIMIT 1', network_name=network_name))
        if not networks['records']:
            raise SalesforceException(f'No Network record found with Name "{network_name}"')
        self.logger.info(f'Creating NetworkMemberGroup records for {network_name} Network:')
        return networks['records'][0]['Id']
<|end_body_0|>

<|body_start_1|>
        network_member_group_parent_ids = set()
        for record in self.sf.query_all(f"SELECT ParentId FROM NetworkMemberGroup WHERE NetworkId = '{network_id}'")['records']:
            network_member_group_parent_ids.add(record['ParentId'])
        return network_member_group_parent_ids
<|end_body_1|>

<|body_start_2|>
        parent_ids_by_name = dict(((name, None) for name in record_names))
        if sobject_type == 'PermissionSet':
            field_key = 'Label'
        else:
            field_key = 'Name'
        for record in self.sf.query_all("SELECT Id, {} FROM {} WHERE {} IN ('{}')".format(field_key, sobject_type, field_key, "','".join(record_names)))['records']:
            record_name = record[field_key]
            parent_ids_by_name[record_name] = record['Id']
        return parent_ids_by_name
<|end_body_2|>

<|body_start_3|>
        if not record_names:
            return
        self.logger.info(f'    {sobject_type}:')
        parent_ids_by_name = self._get_parent_ids_by_name(sobject_type, record_names)
        for parent_name, parent_id in parent_ids_by_name.items():
            self._create_network_member_group(sobject_type, parent_name, parent_id)
<|end_body_3|>

<|body_start_4|>
        if not parent_id:
            raise CumulusCIException(f'No {sobject_type} record found with Name "{parent_name}"')
        if parent_id in self._parent_ids:
            self.logger.warning(f'        Already exists for "{parent_name}"')
        else:
            insert_response = self.sf.NetworkMemberGroup.create({'NetworkId': self._network_id, 'ParentId': parent_id})
            if insert_response.get('success') is True:
                self.logger.info(f'        "{parent_name}"')
            else:
                raise SalesforceException(f'''Error creating NetworkMemberGroup for Network "{self._network_id}" for parent {sobject_type} "{parent_name}" {parent_id}.   Errors: {', '.join(insert_response.get('errors') or [])}''')
<|end_body_4|>

<|body_start_5|>
        self._network_id = self._get_network_id(self.options['network_name'])
        self._parent_ids = self._get_network_member_group_parent_ids(self._network_id)
        for sobject_type, record_names in {'Profile': process_list_arg(self.options.get('profile_names') or []), 'PermissionSet': process_list_arg(self.options.get('permission_set_names') or [])}.items():
            self._process_parent(sobject_type, record_names)
<|end_body_5|>

Solution:
class CreateNetworkMemberGroups:
    """Creates NetworkMemberGroup for a Network (Experience Site) for Profiles and Permission Sets that don't already have a corresponding NetworkMemberGroup. Raises exceptions if records cannot be found: - Network with Name network_name - Profiles with Names in profile_names - Permission Sets with Names in permission_set_names"""

    def _get_network_id(self, network_name: str) -> str:
        """Returns Id of Network record with Name network_name. Raises a SalesforceException if no Network is found."""
        networks = self.sf.query_all(format_soql('SELECT Id FROM Network WHERE Name = {network_name} LIMIT 1', network_name=network_name))
        if not networks['records']:
            raise SalesforceException(f'No Network record found with Name "{network_name}"')
        self.logger.info(f'Creating NetworkMemberGroup records for {network_name} Network:')
        return networks['records'][0]['Id']

    def _get_network_member_group_parent_ids(self, network_id) -> set:
        """Collect existing NetworkMemberGroup Parent IDs (associated Profile or Permission Set ID). An excpetion is thrown trying to create a NetworkMemberGroup for a parent who already has a record."""
        network_member_group_parent_ids = set()
        for record in self.sf.query_all(f"SELECT ParentId FROM NetworkMemberGroup WHERE NetworkId = '{network_id}'")['records']:
            network_member_group_parent_ids.add(record['ParentId'])
        return network_member_group_parent_ids

    def _get_parent_ids_by_name(self, sobject_type: str, record_names: List[str]):
        """Returns a Dict: Name --> ID of records with Name in record_names for sObject_type. Dict value are None for all record_names that do not have corresponding records."""
        parent_ids_by_name = dict(((name, None) for name in record_names))
        if sobject_type == 'PermissionSet':
            field_key = 'Label'
        else:
            field_key = 'Name'
        for record in self.sf.query_all("SELECT Id, {} FROM {} WHERE {} IN ('{}')".format(field_key, sobject_type, field_key, "','".join(record_names)))['records']:
            record_name = record[field_key]
            parent_ids_by_name[record_name] = record['Id']
        return parent_ids_by_name

    def _process_parent(self, sobject_type, record_names) -> None:
        """For a specific sobject_type and record_names, queries all Salesforce IDs corresponding to records of SObjectType sobject_type with Name in record_names. Then, tries to create NetworkMemberGroup for each parent in record_names."""
        if not record_names:
            return
        self.logger.info(f'    {sobject_type}:')
        parent_ids_by_name = self._get_parent_ids_by_name(sobject_type, record_names)
        for parent_name, parent_id in parent_ids_by_name.items():
            self._create_network_member_group(sobject_type, parent_name, parent_id)

    def _create_network_member_group(self, sobject_type, parent_name, parent_id) -> None:
        """Processes and logs creating a NetworkMemberGroup for a specific parent. Outcomes: - Raises a CumulusCIException if record_id is None meaning no corresponding record was found in _get_parent_ids_by_name. - Logs a warning that a NetworkMemberGroup already exists is parent_id is in self._parent_ids. - Creates a NetworkMemberGroup for parent_id and logs the result."""
        if not parent_id:
            raise CumulusCIException(f'No {sobject_type} record found with Name "{parent_name}"')
        if parent_id in self._parent_ids:
            self.logger.warning(f'        Already exists for "{parent_name}"')
        else:
            insert_response = self.sf.NetworkMemberGroup.create({'NetworkId': self._network_id, 'ParentId': parent_id})
            if insert_response.get('success') is True:
                self.logger.info(f'        "{parent_name}"')
            else:
                raise SalesforceException(f'''Error creating NetworkMemberGroup for Network "{self._network_id}" for parent {sobject_type} "{parent_name}" {parent_id}.   Errors: {', '.join(insert_response.get('errors') or [])}''')

    def _run_task(self):
        """Gets required information then tries to create NetworkMemberGroups for Profiles and Permission Sets cooresponding to profile_names and permission_set_names respectively."""
        self._network_id = self._get_network_id(self.options['network_name'])
        self._parent_ids = self._get_network_member_group_parent_ids(self._network_id)
        for sobject_type, record_names in {'Profile': process_list_arg(self.options.get('profile_names') or []), 'PermissionSet': process_list_arg(self.options.get('permission_set_names') or [])}.items():
            self._process_parent(sobject_type, record_names)


moveZeroes

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def moveZeroes(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def moveZeroes(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.

<|skeleton|>
class Solution:

    def moveZeroes(self, nums: List[int]) -> None:
        """Do not return anything, modify nums in-place instead."""
        <|body_0|>

    def moveZeroes2(self, nums: List[int]) -> None:
        """Do not return anything, modify nums in-place instead."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            j = nums.index(0)
        except ValueError:
            return
        nums_len = len(nums)
        i = j + 1
        while i < nums_len and nums[i] == 0:
            i += 1
        while i < nums_len:
            nums[j] = nums[i]
            nums[i] = 0
            j += 1
            while i < nums_len and nums[i] == 0:
                i += 1
<|end_body_0|>

<|body_start_1|>
        i = 0
        for num in nums:
            if num != 0:
                nums[i] = num
                i += 1
        while i < len(nums):
            nums[i] = 0
            i += 1
<|end_body_1|>

Solution:
class Solution:
    def moveZeroes(self, nums: List[int]) -> None:
        """Do not return anything, modify nums in-place instead."""
        try:
            j = nums.index(0)
        except ValueError:
            return
        nums_len = len(nums)
        i = j + 1
        while i < nums_len and nums[i] == 0:
            i += 1
        while i < nums_len:
            nums[j] = nums[i]
            nums[i] = 0
            j += 1
            while i < nums_len and nums[i] == 0:
                i += 1

    def moveZeroes2(self, nums: List[int]) -> None:
        """Do not return anything, modify nums in-place instead."""
        i = 0
        for num in nums:
            if num != 0:
                nums[i] = num
                i += 1
        while i < len(nums):
            nums[i] = 0
            i += 1


call

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

Class description:
Selects a chunk according to a Boltzmann distribution.

Method signatures and docstrings:
- def __init__(self, source, temperature=0.01, threshold=0.25): Initialize a ``BoltzmannSelector`` instance. :param temperature: Temperature of the Boltzmann distribution.
- def call(self, inputs): Select chunks through an activation-based competition. Selection probabilities vary with chunk strengths according to a Boltzmann distribution.

Implement the Python class `BoltzmannSelector` described below.

Class description:
Selects a chunk according to a Boltzmann distribution.

Method signatures and docstrings:
- def __init__(self, source, temperature=0.01, threshold=0.25): Initialize a ``BoltzmannSelector`` instance. :param temperature: Temperature of the Boltzmann distribution.
- def call(self, inputs): Select chunks through an activation-based competition. Selection probabilities vary with chunk strengths according to a Boltzmann distribution.

<|skeleton|>
class BoltzmannSelector:
    """Selects a chunk according to a Boltzmann distribution."""

    def __init__(self, source, temperature=0.01, threshold=0.25):
        """Initialize a ``BoltzmannSelector`` instance. :param temperature: Temperature of the Boltzmann distribution."""
        <|body_0|>

    def call(self, inputs):
        """Select chunks through an activation-based competition. Selection probabilities vary with chunk strengths according to a Boltzmann distribution."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(expected=(source,))
        self.temperature = temperature
        self.threshold = threshold
<|end_body_0|>

<|body_start_1|>
        strengths, = self.extract_inputs(inputs)
        thresholded = nd.threshold(strengths, th=self.threshold)
        probabilities = nd.boltzmann(thresholded, self.temperature)
        d = nd.draw(probabilities, n=1)
        d = nd.with_default(d, default=0)
        return d
<|end_body_1|>

Solution:
class BoltzmannSelector:
    """Selects a chunk according to a Boltzmann distribution."""

    def __init__(self, source, temperature=0.01, threshold=0.25):
        """Initialize a ``BoltzmannSelector`` instance. :param temperature: Temperature of the Boltzmann distribution."""
        super().__init__(expected=(source,))
        self.temperature = temperature
        self.threshold = threshold

    def call(self, inputs):
        """Select chunks through an activation-based competition. Selection probabilities vary with chunk strengths according to a Boltzmann distribution."""
        strengths, = self.extract_inputs(inputs)
        thresholded = nd.threshold(strengths, th=self.threshold)
        probabilities = nd.boltzmann(thresholded, self.temperature)
        d = nd.draw(probabilities, n=1)
        d = nd.with_default(d, default=0)
        return d