explain

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

Class description:
Wrapper around the LIME explainer. Lime explainer byMarco Tulio Correia Ribeiro (https://github.com/marcotcr/lime).

Method signatures and docstrings:
- def __init__(self, kernel_width=25, kernel=None, verbose=False, feature_selection='auto', random_state=None, axis_labels=None, preprocess_function=None): Initializes Lime explainer. Args: kernel_width (int, optional): kernel width kernel (callable, optional): kernel verbose (bool, optional): verbose feature_selection (str, optional): feature selection random_state (int or np.RandomState, optional): seed or random state axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name. If a list, the name of each axis where the index in the list is the axis index preprocess_function (callable, optional): Function to preprocess input data with
- def explain(self, model_or_function, input_data, labels, top_labels=None, num_features=10, num_samples=5000, return_masks=True, positive_only=False, hide_rest=True, **kwargs): Run the LIME explainer. Args: model_or_function (callable or str): The function that runs the model to be explained _or_ the path to a ONNX model on disk. input_data (np.ndarray): Data to be explained. Must be an "RGB image", i.e. with values in the [0,255] range. labels (Iterable(int)): Indices of classes to be explained top_labels: Top labels num_features (int): Number of features num_samples (int): Number of samples return_masks (bool): If true, return discretized masks. Otherwise, return LIME scores positive_only (bool): Positive only hide_rest (bool): Hide rest kwargs: These parameters are passed on Other keyword arguments: see the LIME documentation for LimeImageExplainer.explain_insta
- def _prepare_image_data(self, input_data): Transforms the data to be of the shape and type LIME expects. Args: input_data (NumPy-compatible array): Data to be explained Returns: transformed input data, preprocessing function to use with utils.get_function()
- def _get_full_preprocess_function(self, channel_axis_index, dtype, greyscale=False): Creates a full preprocessing function. Creates a preprocessing function that incorporates both the (optional) user's preprocessing function, as well as any needed dtype and shape conversions Args: channel_axis_index (int): Axis index of the channels in the input data dtype (type): Data type of the input data (e.g. np.float32) greyscale (bool): Whether or not the data is greyscale (i.e. one channel) Returns: Function that first ensures the data has the same shape and type as the input data, then runs the users' preprocessing function
- def _get_explanation_values(self, label: int, explanation: ImageExplanation) -> np.array: Get the importance scores from LIME in a salience map. Leverages the `ImageExplanation` class from LIME to generate salience maps. These salience maps are constructed using the segmentation masks from the explanation and fills these with the scores from the surrogate model (default for LIME is Ridge regression) used for the explanation. Args: label: The class label for the given explanation explanation: An Image Explanation generated by LIME Returns: A salience map containing the feature importances from LIME

Implement the Python class `LIMEImage` described below.

Class description:
Wrapper around the LIME explainer. Lime explainer byMarco Tulio Correia Ribeiro (https://github.com/marcotcr/lime).

Method signatures and docstrings:
- def __init__(self, kernel_width=25, kernel=None, verbose=False, feature_selection='auto', random_state=None, axis_labels=None, preprocess_function=None): Initializes Lime explainer. Args: kernel_width (int, optional): kernel width kernel (callable, optional): kernel verbose (bool, optional): verbose feature_selection (str, optional): feature selection random_state (int or np.RandomState, optional): seed or random state axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name. If a list, the name of each axis where the index in the list is the axis index preprocess_function (callable, optional): Function to preprocess input data with
- def explain(self, model_or_function, input_data, labels, top_labels=None, num_features=10, num_samples=5000, return_masks=True, positive_only=False, hide_rest=True, **kwargs): Run the LIME explainer. Args: model_or_function (callable or str): The function that runs the model to be explained _or_ the path to a ONNX model on disk. input_data (np.ndarray): Data to be explained. Must be an "RGB image", i.e. with values in the [0,255] range. labels (Iterable(int)): Indices of classes to be explained top_labels: Top labels num_features (int): Number of features num_samples (int): Number of samples return_masks (bool): If true, return discretized masks. Otherwise, return LIME scores positive_only (bool): Positive only hide_rest (bool): Hide rest kwargs: These parameters are passed on Other keyword arguments: see the LIME documentation for LimeImageExplainer.explain_insta
- def _prepare_image_data(self, input_data): Transforms the data to be of the shape and type LIME expects. Args: input_data (NumPy-compatible array): Data to be explained Returns: transformed input data, preprocessing function to use with utils.get_function()
- def _get_full_preprocess_function(self, channel_axis_index, dtype, greyscale=False): Creates a full preprocessing function. Creates a preprocessing function that incorporates both the (optional) user's preprocessing function, as well as any needed dtype and shape conversions Args: channel_axis_index (int): Axis index of the channels in the input data dtype (type): Data type of the input data (e.g. np.float32) greyscale (bool): Whether or not the data is greyscale (i.e. one channel) Returns: Function that first ensures the data has the same shape and type as the input data, then runs the users' preprocessing function
- def _get_explanation_values(self, label: int, explanation: ImageExplanation) -> np.array: Get the importance scores from LIME in a salience map. Leverages the `ImageExplanation` class from LIME to generate salience maps. These salience maps are constructed using the segmentation masks from the explanation and fills these with the scores from the surrogate model (default for LIME is Ridge regression) used for the explanation. Args: label: The class label for the given explanation explanation: An Image Explanation generated by LIME Returns: A salience map containing the feature importances from LIME

<|skeleton|>
class LIMEImage:
    """Wrapper around the LIME explainer. Lime explainer byMarco Tulio Correia Ribeiro (https://github.com/marcotcr/lime)."""

    def __init__(self, kernel_width=25, kernel=None, verbose=False, feature_selection='auto', random_state=None, axis_labels=None, preprocess_function=None):
        """Initializes Lime explainer. Args: kernel_width (int, optional): kernel width kernel (callable, optional): kernel verbose (bool, optional): verbose feature_selection (str, optional): feature selection random_state (int or np.RandomState, optional): seed or random state axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name. If a list, the name of each axis where the index in the list is the axis index preprocess_function (callable, optional): Function to preprocess input data with"""
        <|body_0|>

    def explain(self, model_or_function, input_data, labels, top_labels=None, num_features=10, num_samples=5000, return_masks=True, positive_only=False, hide_rest=True, **kwargs):
        """Run the LIME explainer. Args: model_or_function (callable or str): The function that runs the model to be explained _or_ the path to a ONNX model on disk. input_data (np.ndarray): Data to be explained. Must be an "RGB image", i.e. with values in the [0,255] range. labels (Iterable(int)): Indices of classes to be explained top_labels: Top labels num_features (int): Number of features num_samples (int): Number of samples return_masks (bool): If true, return discretized masks. Otherwise, return LIME scores positive_only (bool): Positive only hide_rest (bool): Hide rest kwargs: These parameters are passed on Other keyword arguments: see the LIME documentation for LimeImageExplainer.explain_insta"""
        <|body_1|>

    def _prepare_image_data(self, input_data):
        """Transforms the data to be of the shape and type LIME expects. Args: input_data (NumPy-compatible array): Data to be explained Returns: transformed input data, preprocessing function to use with utils.get_function()"""
        <|body_2|>

    def _get_full_preprocess_function(self, channel_axis_index, dtype, greyscale=False):
        """Creates a full preprocessing function. Creates a preprocessing function that incorporates both the (optional) user's preprocessing function, as well as any needed dtype and shape conversions Args: channel_axis_index (int): Axis index of the channels in the input data dtype (type): Data type of the input data (e.g. np.float32) greyscale (bool): Whether or not the data is greyscale (i.e. one channel) Returns: Function that first ensures the data has the same shape and type as the input data, then runs the users' preprocessing function"""
        <|body_3|>

    def _get_explanation_values(self, label: int, explanation: ImageExplanation) -> np.array:
        """Get the importance scores from LIME in a salience map. Leverages the `ImageExplanation` class from LIME to generate salience maps. These salience maps are constructed using the segmentation masks from the explanation and fills these with the scores from the surrogate model (default for LIME is Ridge regression) used for the explanation. Args: label: The class label for the given explanation explanation: An Image Explanation generated by LIME Returns: A salience map containing the feature importances from LIME"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.preprocess_function = preprocess_function
        self.axis_labels = axis_labels if axis_labels is not None else []
        self.explainer = LimeImageExplainer(kernel_width, kernel, verbose, feature_selection, random_state)
<|end_body_0|>

<|body_start_1|>
        input_data, full_preprocess_function = self._prepare_image_data(input_data)
        runner = utils.get_function(model_or_function, preprocess_function=full_preprocess_function)
        explain_instance_kwargs = utils.get_kwargs_applicable_to_function(self.explainer.explain_instance, kwargs)
        explanation = self.explainer.explain_instance(input_data, runner, labels=labels, top_labels=top_labels, num_features=num_features, num_samples=num_samples, **explain_instance_kwargs)
        if return_masks:
            get_image_and_mask_kwargs = utils.get_kwargs_applicable_to_function(explanation.get_image_and_mask, kwargs)
            maps = [explanation.get_image_and_mask(label, positive_only=positive_only, hide_rest=hide_rest, num_features=num_features, **get_image_and_mask_kwargs)[1] for label in labels]
        else:
            maps = [self._get_explanation_values(label, explanation) for label in labels]
        return maps
<|end_body_1|>

<|body_start_2|>
        axis_label_names = self.axis_labels.values() if isinstance(self.axis_labels, dict) else self.axis_labels
        if not axis_label_names:
            channels_axis_index = utils.locate_channels_axis(input_data.shape)
            self.axis_labels = {channels_axis_index: 'channels'}
        elif 'channels' not in axis_label_names:
            raise ValueError('When providing axis_labels it is required to provide the location of the channels axis')
        input_data = utils.to_xarray(input_data, self.axis_labels)
        channels_axis_index = input_data.dims.index('channels')
        input_data = utils.move_axis(input_data, 'channels', -1)
        greyscale = False
        if len(input_data['channels']) == 1:
            greyscale = True
            input_data = input_data.sel(channels=0).expand_dims({'channels': 3}, axis=input_data.dims.index('channels'))
        full_preprocess_function = self._get_full_preprocess_function(channels_axis_index, input_data.dtype, greyscale)
        return (input_data.values.astype(np.float64), full_preprocess_function)
<|end_body_2|>

<|body_start_3|>
        if greyscale:

            def moveaxis_function(data):
                return np.moveaxis(data[..., [0]], -1, channel_axis_index + 1).astype(dtype)
        else:

            def moveaxis_function(data):
                return np.moveaxis(data, -1, channel_axis_index + 1).astype(dtype)
        if self.preprocess_function is None:
            return moveaxis_function
        return lambda data: self.preprocess_function(moveaxis_function(data))
<|end_body_3|>

<|body_start_4|>
        class_explanation = explanation.local_exp[label]
        salience_map = np.zeros(explanation.segments.shape, dtype=class_explanation[0][1].dtype)
        for segment_id, segment_val in class_explanation:
            salience_map[segment_id == explanation.segments] = segment_val
        return salience_map
<|end_body_4|>

Solution:
class LIMEImage:
    """Wrapper around the LIME explainer. Lime explainer byMarco Tulio Correia Ribeiro (https://github.com/marcotcr/lime)."""

    def __init__(self, kernel_width=25, kernel=None, verbose=False, feature_selection='auto', random_state=None, axis_labels=None, preprocess_function=None):
        """Initializes Lime explainer. Args: kernel_width (int, optional): kernel width kernel (callable, optional): kernel verbose (bool, optional): verbose feature_selection (str, optional): feature selection random_state (int or np.RandomState, optional): seed or random state axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name. If a list, the name of each axis where the index in the list is the axis index preprocess_function (callable, optional): Function to preprocess input data with"""
        self.preprocess_function = preprocess_function
        self.axis_labels = axis_labels if axis_labels is not None else []
        self.explainer = LimeImageExplainer(kernel_width, kernel, verbose, feature_selection, random_state)

    def explain(self, model_or_function, input_data, labels, top_labels=None, num_features=10, num_samples=5000, return_masks=True, positive_only=False, hide_rest=True, **kwargs):
        """Run the LIME explainer. Args: model_or_function (callable or str): The function that runs the model to be explained _or_ the path to a ONNX model on disk. input_data (np.ndarray): Data to be explained. Must be an "RGB image", i.e. with values in the [0,255] range. labels (Iterable(int)): Indices of classes to be explained top_labels: Top labels num_features (int): Number of features num_samples (int): Number of samples return_masks (bool): If true, return discretized masks. Otherwise, return LIME scores positive_only (bool): Positive only hide_rest (bool): Hide rest kwargs: These parameters are passed on Other keyword arguments: see the LIME documentation for LimeImageExplainer.explain_insta"""
        input_data, full_preprocess_function = self._prepare_image_data(input_data)
        runner = utils.get_function(model_or_function, preprocess_function=full_preprocess_function)
        explain_instance_kwargs = utils.get_kwargs_applicable_to_function(self.explainer.explain_instance, kwargs)
        explanation = self.explainer.explain_instance(input_data, runner, labels=labels, top_labels=top_labels, num_features=num_features, num_samples=num_samples, **explain_instance_kwargs)
        if return_masks:
            get_image_and_mask_kwargs = utils.get_kwargs_applicable_to_function(explanation.get_image_and_mask, kwargs)
            maps = [explanation.get_image_and_mask(label, positive_only=positive_only, hide_rest=hide_rest, num_features=num_features, **get_image_and_mask_kwargs)[1] for label in labels]
        else:
            maps = [self._get_explanation_values(label, explanation) for label in labels]
        return maps

    def _prepare_image_data(self, input_data):
        """Transforms the data to be of the shape and type LIME expects. Args: input_data (NumPy-compatible array): Data to be explained Returns: transformed input data, preprocessing function to use with utils.get_function()"""
        axis_label_names = self.axis_labels.values() if isinstance(self.axis_labels, dict) else self.axis_labels
        if not axis_label_names:
            channels_axis_index = utils.locate_channels_axis(input_data.shape)
            self.axis_labels = {channels_axis_index: 'channels'}
        elif 'channels' not in axis_label_names:
            raise ValueError('When providing axis_labels it is required to provide the location of the channels axis')
        input_data = utils.to_xarray(input_data, self.axis_labels)
        channels_axis_index = input_data.dims.index('channels')
        input_data = utils.move_axis(input_data, 'channels', -1)
        greyscale = False
        if len(input_data['channels']) == 1:
            greyscale = True
            input_data = input_data.sel(channels=0).expand_dims({'channels': 3}, axis=input_data.dims.index('channels'))
        full_preprocess_function = self._get_full_preprocess_function(channels_axis_index, input_data.dtype, greyscale)
        return (input_data.values.astype(np.float64), full_preprocess_function)

    def _get_full_preprocess_function(self, channel_axis_index, dtype, greyscale=False):
        """Creates a full preprocessing function. Creates a preprocessing function that incorporates both the (optional) user's preprocessing function, as well as any needed dtype and shape conversions Args: channel_axis_index (int): Axis index of the channels in the input data dtype (type): Data type of the input data (e.g. np.float32) greyscale (bool): Whether or not the data is greyscale (i.e. one channel) Returns: Function that first ensures the data has the same shape and type as the input data, then runs the users' preprocessing function"""
        if greyscale:

            def moveaxis_function(data):
                return np.moveaxis(data[..., [0]], -1, channel_axis_index + 1).astype(dtype)
        else:

            def moveaxis_function(data):
                return np.moveaxis(data, -1, channel_axis_index + 1).astype(dtype)
        if self.preprocess_function is None:
            return moveaxis_function
        return lambda data: self.preprocess_function(moveaxis_function(data))

    def _get_explanation_values(self, label: int, explanation: ImageExplanation) -> np.array:
        """Get the importance scores from LIME in a salience map. Leverages the `ImageExplanation` class from LIME to generate salience maps. These salience maps are constructed using the segmentation masks from the explanation and fills these with the scores from the surrogate model (default for LIME is Ridge regression) used for the explanation. Args: label: The class label for the given explanation explanation: An Image Explanation generated by LIME Returns: A salience map containing the feature importances from LIME"""
        class_explanation = explanation.local_exp[label]
        salience_map = np.zeros(explanation.segments.shape, dtype=class_explanation[0][1].dtype)
        for segment_id, segment_val in class_explanation:
            salience_map[segment_id == explanation.segments] = segment_val
        return salience_map


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

Class description:
Implement the AnnotationModelTest class.

Method signatures and docstrings:
- def test_forward_wellformedness(self): Tests whether the outout of the forward function of AnnotationModel is the one-hot representation of the indices passed to it
- def test_pad_token_zeroed(self): Tests whether the pad token (0) is given the zero vector, unlike all other token indices
- def test_trainable_is_respected(self): Tests whether the trainable flag, which is True if a models should be fine-tuned during training, and False otherwise, is respected. That is, the embedding weights are trained (or not trained) depending on the boolean value of trainable

Implement the Python class `AnnotationModelTest` described below.

Class description:
Implement the AnnotationModelTest class.

Method signatures and docstrings:
- def test_forward_wellformedness(self): Tests whether the outout of the forward function of AnnotationModel is the one-hot representation of the indices passed to it
- def test_pad_token_zeroed(self): Tests whether the pad token (0) is given the zero vector, unlike all other token indices
- def test_trainable_is_respected(self): Tests whether the trainable flag, which is True if a models should be fine-tuned during training, and False otherwise, is respected. That is, the embedding weights are trained (or not trained) depending on the boolean value of trainable

<|skeleton|>
class AnnotationModelTest:

    def test_forward_wellformedness(self):
        """Tests whether the outout of the forward function of AnnotationModel is the one-hot representation of the indices passed to it"""
        <|body_0|>

    def test_pad_token_zeroed(self):
        """Tests whether the pad token (0) is given the zero vector, unlike all other token indices"""
        <|body_1|>

    def test_trainable_is_respected(self):
        """Tests whether the trainable flag, which is True if a models should be fine-tuned during training, and False otherwise, is respected. That is, the embedding weights are trained (or not trained) depending on the boolean value of trainable"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        max_cardinality = 4
        trainable = False
        args = {'device': 'cpu'}
        annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
        annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
        alignment_tensor = None
        output = annotation_model((annotation_tensor, alignment_tensor))
        batch_size = 3
        seq_len = 5
        self.assertEqual(output.shape, (batch_size, seq_len, max_cardinality))
        self.assert_annotations_equal([0, 1, 0, 0], output[0, 0])
        self.assert_annotations_equal([0, 0, 0, 1], output[1, 4])
<|end_body_0|>

<|body_start_1|>
        max_cardinality = 4
        trainable = False
        args = {'device': 'cpu'}
        annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
        annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
        alignment_tensor = None
        output = annotation_model((annotation_tensor, alignment_tensor))
        self.assert_annotations_equal([0, 0, 0, 0], output[0, 4])
        self.assert_annotations_equal([0, 0, 0, 0], output[2, 2])
<|end_body_1|>

<|body_start_2|>
        max_cardinality = 4
        args = {'device': 'cpu'}
        for trainable in (False, True):
            annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
            optimizer = torch.optim.SGD(annotation_model.embeddings.parameters(), lr=0.01)
            weights = torch.nn.Parameter(torch.zeros(max_cardinality))
            annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
            alignment_tensor = None
            output = annotation_model((annotation_tensor, alignment_tensor))
            nn.init.uniform_(weights)
            prediction = torch.dot(weights, output[0, 0])
            loss = (prediction - torch.tensor(1)) ** 2
            loss.backward()
            optimizer.step()
            new_output = annotation_model((annotation_tensor, alignment_tensor))
            new_prediction = torch.dot(weights, new_output[0, 0])
            new_loss = (new_prediction - torch.tensor(1)) ** 2
            if not trainable:
                self.assert_annotations_equal([0, 1, 0, 0], annotation_model.embeddings.weight.data[1, :])
                self.assertAlmostEqual(new_loss, loss, places=7)
            else:
                self.assertLess(new_loss, loss)
<|end_body_2|>

Solution:
class AnnotationModelTest:
    def test_forward_wellformedness(self):
        """Tests whether the outout of the forward function of AnnotationModel is the one-hot representation of the indices passed to it"""
        max_cardinality = 4
        trainable = False
        args = {'device': 'cpu'}
        annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
        annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
        alignment_tensor = None
        output = annotation_model((annotation_tensor, alignment_tensor))
        batch_size = 3
        seq_len = 5
        self.assertEqual(output.shape, (batch_size, seq_len, max_cardinality))
        self.assert_annotations_equal([0, 1, 0, 0], output[0, 0])
        self.assert_annotations_equal([0, 0, 0, 1], output[1, 4])

    def test_pad_token_zeroed(self):
        """Tests whether the pad token (0) is given the zero vector, unlike all other token indices"""
        max_cardinality = 4
        trainable = False
        args = {'device': 'cpu'}
        annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
        annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
        alignment_tensor = None
        output = annotation_model((annotation_tensor, alignment_tensor))
        self.assert_annotations_equal([0, 0, 0, 0], output[0, 4])
        self.assert_annotations_equal([0, 0, 0, 0], output[2, 2])

    def test_trainable_is_respected(self):
        """Tests whether the trainable flag, which is True if a models should be fine-tuned during training, and False otherwise, is respected. That is, the embedding weights are trained (or not trained) depending on the boolean value of trainable"""
        max_cardinality = 4
        args = {'device': 'cpu'}
        for trainable in (False, True):
            annotation_model = model.AnnotationModel(args, max_cardinality, trainable)
            optimizer = torch.optim.SGD(annotation_model.embeddings.parameters(), lr=0.01)
            weights = torch.nn.Parameter(torch.zeros(max_cardinality))
            annotation_tensor = torch.tensor([[1, 2, 1, 3, 0], [3, 3, 3, 3, 3], [2, 1, 0, 0, 0]])
            alignment_tensor = None
            output = annotation_model((annotation_tensor, alignment_tensor))
            nn.init.uniform_(weights)
            prediction = torch.dot(weights, output[0, 0])
            loss = (prediction - torch.tensor(1)) ** 2
            loss.backward()
            optimizer.step()
            new_output = annotation_model((annotation_tensor, alignment_tensor))
            new_prediction = torch.dot(weights, new_output[0, 0])
            new_loss = (new_prediction - torch.tensor(1)) ** 2
            if not trainable:
                self.assert_annotations_equal([0, 1, 0, 0], annotation_model.embeddings.weight.data[1, :])
                self.assertAlmostEqual(new_loss, loss, places=7)
            else:
                self.assertLess(new_loss, loss)


update

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

Class description:
Private class. Information for a submember node that wraps a single object.

Method signatures and docstrings:
- def __init__(self, subMember, tree, container, parentTreeItemId): Create a new node inside the tree that wraps the specified submember
- def getObject(self): Returns the object associated with this node, or None if no object is associated.
- def update(self, object): Updates the node so that it contains the specified object.

Implement the Python class `subMemberObjectNode` described below.

Class description:
Private class. Information for a submember node that wraps a single object.

Method signatures and docstrings:
- def __init__(self, subMember, tree, container, parentTreeItemId): Create a new node inside the tree that wraps the specified submember
- def getObject(self): Returns the object associated with this node, or None if no object is associated.
- def update(self, object): Updates the node so that it contains the specified object.

<|skeleton|>
class subMemberObjectNode:
    """Private class. Information for a submember node that wraps a single object."""

    def __init__(self, subMember, tree, container, parentTreeItemId):
        """Create a new node inside the tree that wraps the specified submember"""
        <|body_0|>

    def getObject(self):
        """Returns the object associated with this node, or None if no object is associated."""
        <|body_1|>

    def update(self, object):
        """Updates the node so that it contains the specified object."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(subMemberObjectNode, self).__init__(subMember, tree, container)
        self._treeItemId = tree.AppendItem(parentTreeItemId, '')
        if tree.isAutoVisible():
            tree.EnsureVisible(self._treeItemId)
<|end_body_0|>

<|body_start_1|>
        nodeData = self._tree.GetItemPyData(self._treeItemId)
        if nodeData is None:
            raise AttributeError('No data attached to tree node!')
        return nodeData.getObject()
<|end_body_1|>

<|body_start_2|>
        subObject = self._member.getObject(object)
        try:
            currentObject = self.getObject()
            if PyUtils.sameObject(currentObject, subObject):
                return
        except AttributeError:
            pass
        nodeData = NodeData(self._member.name, subObject, self._tree, self._treeItemId, self._container)
<|end_body_2|>

Solution:
class subMemberObjectNode:
    """Private class. Information for a submember node that wraps a single object."""

    def __init__(self, subMember, tree, container, parentTreeItemId):
        """Create a new node inside the tree that wraps the specified submember"""
        super(subMemberObjectNode, self).__init__(subMember, tree, container)
        self._treeItemId = tree.AppendItem(parentTreeItemId, '')
        if tree.isAutoVisible():
            tree.EnsureVisible(self._treeItemId)

    def getObject(self):
        """Returns the object associated with this node, or None if no object is associated."""
        nodeData = self._tree.GetItemPyData(self._treeItemId)
        if nodeData is None:
            raise AttributeError('No data attached to tree node!')
        return nodeData.getObject()

    def update(self, object):
        """Updates the node so that it contains the specified object."""
        subObject = self._member.getObject(object)
        try:
            currentObject = self.getObject()
            if PyUtils.sameObject(currentObject, subObject):
                return
        except AttributeError:
            pass
        nodeData = NodeData(self._member.name, subObject, self._tree, self._treeItemId, self._container)


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

Class description:
Implement the OfficialSolution class.

Method signatures and docstrings:
- def is_valid_BST(self, root: TreeNode) -> bool: 递归
- def is_valid_BST(self, root: TreeNode) -> bool: 中序遍历

Implement the Python class `OfficialSolution` described below.

Class description:
Implement the OfficialSolution class.

Method signatures and docstrings:
- def is_valid_BST(self, root: TreeNode) -> bool: 递归
- def is_valid_BST(self, root: TreeNode) -> bool: 中序遍历

<|skeleton|>
class OfficialSolution:

    def is_valid_BST(self, root: TreeNode) -> bool:
        """递归"""
        <|body_0|>

    def is_valid_BST(self, root: TreeNode) -> bool:
        """中序遍历"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        def helper(node: TreeNode, lower=float('-inf'), upper=float('inf')) -> bool:
            if node is None:
                return True
            val = node.val
            if val <= lower or val >= upper:
                return False
            if not helper(node.left, lower, val):
                return False
            if not helper(node.right, val, upper):
                return False
            return True
        return helper(root)
<|end_body_0|>

<|body_start_1|>
        stack, inorder = ([], float('-inf'))
        while len(stack) > 0 or root is not None:
            while root is not None:
                stack.append(root)
                root = root.left
            root = stack.pop()
            if root.val <= inorder:
                return False
            inorder = root.val
            root = root.right
        return True
<|end_body_1|>

Solution:
class OfficialSolution:
    def is_valid_BST(self, root: TreeNode) -> bool:
        """递归"""
        def helper(node: TreeNode, lower=float('-inf'), upper=float('inf')) -> bool:
            if node is None:
                return True
            val = node.val
            if val <= lower or val >= upper:
                return False
            if not helper(node.left, lower, val):
                return False
            if not helper(node.right, val, upper):
                return False
            return True
        return helper(root)

    def is_valid_BST(self, root: TreeNode) -> bool:
        """中序遍历"""
        stack, inorder = ([], float('-inf'))
        while len(stack) > 0 or root is not None:
            while root is not None:
                stack.append(root)
                root = root.left
            root = stack.pop()
            if root.val <= inorder:
                return False
            inorder = root.val
            root = root.right
        return True


call

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

Class description:
ADULT decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of of a fully connected layer with ReLU nonlinearity, and a multiheaded layer, one for each categorical feature and a single head for the rest of numerical features. The hidden dimension used in the paper is 128.

Method signatures and docstrings:
- def __init__(self, hidden_dim: int, output_dims: List[int], **kwargs): Constructor. Parameters ---------- hidden_dim Hidden dimension. output_dim List of output dimensions.
- def call(self, x: tf.Tensor, **kwargs) -> List[tf.Tensor]: Forward pass. Parameters ---------- x Input tensor. **kwargs Other arguments. Not used. Returns ------- List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the numerical features if they exist, and the rest of the elements correspond to each categorical feature.

Implement the Python class `ADULTDecoder` described below.

Class description:
ADULT decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of of a fully connected layer with ReLU nonlinearity, and a multiheaded layer, one for each categorical feature and a single head for the rest of numerical features. The hidden dimension used in the paper is 128.

Method signatures and docstrings:
- def __init__(self, hidden_dim: int, output_dims: List[int], **kwargs): Constructor. Parameters ---------- hidden_dim Hidden dimension. output_dim List of output dimensions.
- def call(self, x: tf.Tensor, **kwargs) -> List[tf.Tensor]: Forward pass. Parameters ---------- x Input tensor. **kwargs Other arguments. Not used. Returns ------- List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the numerical features if they exist, and the rest of the elements correspond to each categorical feature.

<|skeleton|>
class ADULTDecoder:
    """ADULT decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of of a fully connected layer with ReLU nonlinearity, and a multiheaded layer, one for each categorical feature and a single head for the rest of numerical features. The hidden dimension used in the paper is 128."""

    def __init__(self, hidden_dim: int, output_dims: List[int], **kwargs):
        """Constructor. Parameters ---------- hidden_dim Hidden dimension. output_dim List of output dimensions."""
        <|body_0|>

    def call(self, x: tf.Tensor, **kwargs) -> List[tf.Tensor]:
        """Forward pass. Parameters ---------- x Input tensor. **kwargs Other arguments. Not used. Returns ------- List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the numerical features if they exist, and the rest of the elements correspond to each categorical feature."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        self.fc1 = keras.layers.Dense(hidden_dim)
        self.fcs = [keras.layers.Dense(dim) for dim in output_dims]
<|end_body_0|>

<|body_start_1|>
        x = tf.nn.relu(self.fc1(x))
        xs = [fc(x) for fc in self.fcs]
        return xs
<|end_body_1|>

Solution:
class ADULTDecoder:
    """ADULT decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of of a fully connected layer with ReLU nonlinearity, and a multiheaded layer, one for each categorical feature and a single head for the rest of numerical features. The hidden dimension used in the paper is 128."""

    def __init__(self, hidden_dim: int, output_dims: List[int], **kwargs):
        """Constructor. Parameters ---------- hidden_dim Hidden dimension. output_dim List of output dimensions."""
        super().__init__(**kwargs)
        self.fc1 = keras.layers.Dense(hidden_dim)
        self.fcs = [keras.layers.Dense(dim) for dim in output_dims]

    def call(self, x: tf.Tensor, **kwargs) -> List[tf.Tensor]:
        """Forward pass. Parameters ---------- x Input tensor. **kwargs Other arguments. Not used. Returns ------- List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the numerical features if they exist, and the rest of the elements correspond to each categorical feature."""
        x = tf.nn.relu(self.fc1(x))
        xs = [fc(x) for fc in self.fcs]
        return xs


new

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

Class description:
Client stores user authorization and authentication.

Method signatures and docstrings:
- def from_key(cls, key: str, session: _Session=None) -> Client: Query by unique `key`.
- def from_user(cls, user_id: int, session: _Session=None) -> Client: Query by unique `user_id`.
- def new(cls, user_id: int, level: int=DEFAULT_CLIENT_LEVEL) -> Tuple[Key, Secret, Client]: Create client credentials for `user` with `level`. Args: user_id (int or `User`): An existing user. level (int): Authorization level (default: `DEFAULT_CLIENT_LEVEL`).
- def new_secret(cls, user_id: int) -> Tuple[Key, Secret]: Generate a new secret (store the hashed value).
- def new_key(cls, user_id: int) -> Tuple[Key, Secret]: Generate a new key and secret (store the hashed value).

Implement the Python class `Client` described below.

Class description:
Client stores user authorization and authentication.

Method signatures and docstrings:
- def from_key(cls, key: str, session: _Session=None) -> Client: Query by unique `key`.
- def from_user(cls, user_id: int, session: _Session=None) -> Client: Query by unique `user_id`.
- def new(cls, user_id: int, level: int=DEFAULT_CLIENT_LEVEL) -> Tuple[Key, Secret, Client]: Create client credentials for `user` with `level`. Args: user_id (int or `User`): An existing user. level (int): Authorization level (default: `DEFAULT_CLIENT_LEVEL`).
- def new_secret(cls, user_id: int) -> Tuple[Key, Secret]: Generate a new secret (store the hashed value).
- def new_key(cls, user_id: int) -> Tuple[Key, Secret]: Generate a new key and secret (store the hashed value).

<|skeleton|>
class Client:
    """Client stores user authorization and authentication."""

    def from_key(cls, key: str, session: _Session=None) -> Client:
        """Query by unique `key`."""
        <|body_0|>

    def from_user(cls, user_id: int, session: _Session=None) -> Client:
        """Query by unique `user_id`."""
        <|body_1|>

    def new(cls, user_id: int, level: int=DEFAULT_CLIENT_LEVEL) -> Tuple[Key, Secret, Client]:
        """Create client credentials for `user` with `level`. Args: user_id (int or `User`): An existing user. level (int): Authorization level (default: `DEFAULT_CLIENT_LEVEL`)."""
        <|body_2|>

    def new_secret(cls, user_id: int) -> Tuple[Key, Secret]:
        """Generate a new secret (store the hashed value)."""
        <|body_3|>

    def new_key(cls, user_id: int) -> Tuple[Key, Secret]:
        """Generate a new key and secret (store the hashed value)."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        try:
            session = session or _Session()
            return session.query(cls).filter(cls.key == key).one()
        except NoResultFound as error:
            raise Client.NotFound(f'No client with key={key}') from error
<|end_body_0|>

<|body_start_1|>
        try:
            session = session or _Session()
            return session.query(cls).filter(cls.user_id == user_id).one()
        except NoResultFound as error:
            raise Client.NotFound(f'No client with user_id={user_id}') from error
<|end_body_1|>

<|body_start_2|>
        session = _Session()
        user = User.from_id(user_id, session)
        key, secret = (Key.generate(), Secret.generate())
        client = Client(user_id=user.id, level=level, key=key.value, secret=secret.hashed().value, valid=True)
        session.add(client)
        session.commit()
        log.info(f'Added client for user ({user.id})')
        return (key, secret, client)
<|end_body_2|>

<|body_start_3|>
        session = _Session()
        client = Client.from_user(user_id, session)
        secret = Secret.generate()
        client.secret = secret.hashed().value
        session.commit()
        log.info(f'Updated client secret for user ({client.user_id})')
        return (Key(client.key), secret)
<|end_body_3|>

<|body_start_4|>
        session = _Session()
        client = Client.from_user(user_id, session)
        key, secret = (Key.generate(), Secret.generate())
        client.key = key.value
        client.secret = secret.hashed().value
        session.commit()
        log.info(f'Updated client key and secret for user ({client.user_id})')
        return (key, secret)
<|end_body_4|>

Solution:
class Client:
    """Client stores user authorization and authentication."""

    def from_key(cls, key: str, session: _Session=None) -> Client:
        """Query by unique `key`."""
        try:
            session = session or _Session()
            return session.query(cls).filter(cls.key == key).one()
        except NoResultFound as error:
            raise Client.NotFound(f'No client with key={key}') from error

    def from_user(cls, user_id: int, session: _Session=None) -> Client:
        """Query by unique `user_id`."""
        try:
            session = session or _Session()
            return session.query(cls).filter(cls.user_id == user_id).one()
        except NoResultFound as error:
            raise Client.NotFound(f'No client with user_id={user_id}') from error

    def new(cls, user_id: int, level: int=DEFAULT_CLIENT_LEVEL) -> Tuple[Key, Secret, Client]:
        """Create client credentials for `user` with `level`. Args: user_id (int or `User`): An existing user. level (int): Authorization level (default: `DEFAULT_CLIENT_LEVEL`)."""
        session = _Session()
        user = User.from_id(user_id, session)
        key, secret = (Key.generate(), Secret.generate())
        client = Client(user_id=user.id, level=level, key=key.value, secret=secret.hashed().value, valid=True)
        session.add(client)
        session.commit()
        log.info(f'Added client for user ({user.id})')
        return (key, secret, client)

    def new_secret(cls, user_id: int) -> Tuple[Key, Secret]:
        """Generate a new secret (store the hashed value)."""
        session = _Session()
        client = Client.from_user(user_id, session)
        secret = Secret.generate()
        client.secret = secret.hashed().value
        session.commit()
        log.info(f'Updated client secret for user ({client.user_id})')
        return (Key(client.key), secret)

    def new_key(cls, user_id: int) -> Tuple[Key, Secret]:
        """Generate a new key and secret (store the hashed value)."""
        session = _Session()
        client = Client.from_user(user_id, session)
        key, secret = (Key.generate(), Secret.generate())
        client.key = key.value
        client.secret = secret.hashed().value
        session.commit()
        log.info(f'Updated client key and secret for user ({client.user_id})')
        return (key, secret)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minWindow(self, s, t): :type s: str :type t: str :rtype: str
- def minWindow(self, s, t): :type s: str :type t: str :rtype: str

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minWindow(self, s, t): :type s: str :type t: str :rtype: str
- def minWindow(self, s, t): :type s: str :type t: str :rtype: str

<|skeleton|>
class Solution:

    def minWindow(self, s, t):
        """:type s: str :type t: str :rtype: str"""
        <|body_0|>

    def minWindow(self, s, t):
        """:type s: str :type t: str :rtype: str"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        need, have = ([0] * 128, [0] * 128)
        for ch in t:
            need[ord(ch)] += 1
        l, r, start = (0, 0, 0)
        min_len, count = (float('inf'), 0)
        while r < len(s):
            s_r = ord(s[r])
            if need[s_r] == 0:
                r += 1
                continue
            if have[s_r] < need[s_r]:
                count += 1
            have[s_r] += 1
            r += 1
            while count == len(t):
                if r - l < min_len:
                    min_len = r - l
                    start = l
                s_l = ord(s[l])
                if need[s_l] == 0:
                    l += 1
                    continue
                if have[s_l] == need[s_l]:
                    count -= 1
                have[s_l] -= 1
                l += 1
        return '' if min_len == float('inf') else s[start:start + min_len]
<|end_body_0|>

<|body_start_1|>
        dict_t, counter = (Counter(t), len(t))
        l, r, head = (0, 0, 0)
        min_len = float('inf')
        while r < len(s):
            if s[r] in dict_t:
                if dict_t[s[r]] > 0:
                    counter -= 1
                dict_t[s[r]] -= 1
            r += 1
            while counter == 0:
                if r - l < min_len:
                    min_len = r - l
                    head = l
                if s[l] in dict_t:
                    dict_t[s[l]] += 1
                    if dict_t[s[l]] > 0:
                        counter += 1
                l += 1
        return '' if min_len == float('inf') else s[head:head + min_len]
<|end_body_1|>

Solution:
class Solution:
    def minWindow(self, s, t):
        """:type s: str :type t: str :rtype: str"""
        need, have = ([0] * 128, [0] * 128)
        for ch in t:
            need[ord(ch)] += 1
        l, r, start = (0, 0, 0)
        min_len, count = (float('inf'), 0)
        while r < len(s):
            s_r = ord(s[r])
            if need[s_r] == 0:
                r += 1
                continue
            if have[s_r] < need[s_r]:
                count += 1
            have[s_r] += 1
            r += 1
            while count == len(t):
                if r - l < min_len:
                    min_len = r - l
                    start = l
                s_l = ord(s[l])
                if need[s_l] == 0:
                    l += 1
                    continue
                if have[s_l] == need[s_l]:
                    count -= 1
                have[s_l] -= 1
                l += 1
        return '' if min_len == float('inf') else s[start:start + min_len]

    def minWindow(self, s, t):
        """:type s: str :type t: str :rtype: str"""
        dict_t, counter = (Counter(t), len(t))
        l, r, head = (0, 0, 0)
        min_len = float('inf')
        while r < len(s):
            if s[r] in dict_t:
                if dict_t[s[r]] > 0:
                    counter -= 1
                dict_t[s[r]] -= 1
            r += 1
            while counter == 0:
                if r - l < min_len:
                    min_len = r - l
                    head = l
                if s[l] in dict_t:
                    dict_t[s[l]] += 1
                    if dict_t[s[l]] > 0:
                        counter += 1
                l += 1
        return '' if min_len == float('inf') else s[head:head + min_len]


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

Class description:
Implement the DonationPostTestCase class.

Method signatures and docstrings:
- def test_donation_end_date_in_past(self): date_in_future() returns False for donations with end dates in the past
- def test_donation_end_date_in_present(self): date_in_future() returns True for donations with end dates in the present
- def test_donation_end_date_in_future(self): date_in_future() returns False for donations with end dates in the future

Implement the Python class `DonationPostTestCase` described below.

Class description:
Implement the DonationPostTestCase class.

Method signatures and docstrings:
- def test_donation_end_date_in_past(self): date_in_future() returns False for donations with end dates in the past
- def test_donation_end_date_in_present(self): date_in_future() returns True for donations with end dates in the present
- def test_donation_end_date_in_future(self): date_in_future() returns False for donations with end dates in the future

<|skeleton|>
class DonationPostTestCase:

    def test_donation_end_date_in_past(self):
        """date_in_future() returns False for donations with end dates in the past"""
        <|body_0|>

    def test_donation_end_date_in_present(self):
        """date_in_future() returns True for donations with end dates in the present"""
        <|body_1|>

    def test_donation_end_date_in_future(self):
        """date_in_future() returns False for donations with end dates in the future"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        day = timezone.now() - datetime.timedelta(days=5)
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), False)
<|end_body_0|>

<|body_start_1|>
        day = timezone.now()
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), True)
<|end_body_1|>

<|body_start_2|>
        day = timezone.now() + datetime.timedelta(days=123)
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), True)
<|end_body_2|>

Solution:
class DonationPostTestCase:
    def test_donation_end_date_in_past(self):
        """date_in_future() returns False for donations with end dates in the past"""
        day = timezone.now() - datetime.timedelta(days=5)
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), False)

    def test_donation_end_date_in_present(self):
        """date_in_future() returns True for donations with end dates in the present"""
        day = timezone.now()
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), True)

    def test_donation_end_date_in_future(self):
        """date_in_future() returns False for donations with end dates in the future"""
        day = timezone.now() + datetime.timedelta(days=123)
        past_post = Donation(end_date=day)
        self.assertIs(past_post.date_in_future(), True)


setZeroes

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def setZeroes(self, matrix) -> None: Do not return anything, modify matrix in-place instead.
- def setZeroes(self, matrix) -> None: 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 setZeroes(self, matrix) -> None: Do not return anything, modify matrix in-place instead.
- def setZeroes(self, matrix) -> None: Do not return anything, modify matrix in-place instead.

<|skeleton|>
class Solution:

    def setZeroes(self, matrix) -> None:
        """Do not return anything, modify matrix in-place instead."""
        <|body_0|>

    def setZeroes(self, matrix) -> None:
        """Do not return anything, modify matrix in-place instead."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        m = len(matrix)
        n = len(matrix[0])
        r = []
        c = []
        for x in range(m):
            for i in range(n):
                if matrix[x][i] == 0:
                    r.append(x)
                    c.append(i)
        for x in r:
            for i in range(n):
                matrix[x][i] = 0
        for x in c:
            for i in range(m):
                matrix[i][x] = 0
<|end_body_0|>

<|body_start_1|>
        m = len(matrix)
        n = len(matrix[0])
        r = []
        c = []
        for x in range(m):
            for i in range(n):
                if matrix[x][i] == 0:
                    r.append(x)
                    c.append(i)
        for x in r:
            for i in range(n):
                matrix[x][i] = 0
        for x in c:
            for i in range(m):
                matrix[i][x] = 0
<|end_body_1|>

Solution:
class Solution:
    def setZeroes(self, matrix) -> None:
        """Do not return anything, modify matrix in-place instead."""
        m = len(matrix)
        n = len(matrix[0])
        r = []
        c = []
        for x in range(m):
            for i in range(n):
                if matrix[x][i] == 0:
                    r.append(x)
                    c.append(i)
        for x in r:
            for i in range(n):
                matrix[x][i] = 0
        for x in c:
            for i in range(m):
                matrix[i][x] = 0

    def setZeroes(self, matrix) -> None:
        """Do not return anything, modify matrix in-place instead."""
        m = len(matrix)
        n = len(matrix[0])
        r = []
        c = []
        for x in range(m):
            for i in range(n):
                if matrix[x][i] == 0:
                    r.append(x)
                    c.append(i)
        for x in r:
            for i in range(n):
                matrix[x][i] = 0
        for x in c:
            for i in range(m):
                matrix[i][x] = 0


threeSum

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def twoSum(self, nums, target, leftnum): :type nums: List[int] :type target: int :rtype: List[int]
- def threeSum(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 twoSum(self, nums, target, leftnum): :type nums: List[int] :type target: int :rtype: List[int]
- def threeSum(self, nums): :type nums: List[int] :rtype: List[List[int]]

<|skeleton|>
class Solution:

    def twoSum(self, nums, target, leftnum):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        if len(nums) <= 1:
            return False
        buff_dict = {}
        for i in range(len(nums)):
            if i == leftnum:
                continue
            if nums[i] in buff_dict:
                return [buff_dict[nums[i]], i]
            else:
                buff_dict[target - nums[i]] = i
<|end_body_0|>

<|body_start_1|>
        leftnum = 0
        numstack = []
        for index in range(len(nums)):
            numstack.append(self.twoSum(Solution, nums, -nums[index], index))
        return numstack
<|end_body_1|>

Solution:
class Solution:
    def twoSum(self, nums, target, leftnum):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        if len(nums) <= 1:
            return False
        buff_dict = {}
        for i in range(len(nums)):
            if i == leftnum:
                continue
            if nums[i] in buff_dict:
                return [buff_dict[nums[i]], i]
            else:
                buff_dict[target - nums[i]] = i

    def threeSum(self, nums):
        """:type nums: List[int] :rtype: List[List[int]]"""
        leftnum = 0
        numstack = []
        for index in range(len(nums)):
            numstack.append(self.twoSum(Solution, nums, -nums[index], index))
        return numstack


compute

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

Class description:
Implement the NumberFluctuation class.

Method signatures and docstrings:
- def __init__(self, nsteps, natoms, line): initialize: allocate arrays to store results
- def compute(self, step, xs, ys, plot='False'): compute number fluctuations

Implement the Python class `NumberFluctuation` described below.

Class description:
Implement the NumberFluctuation class.

Method signatures and docstrings:
- def __init__(self, nsteps, natoms, line): initialize: allocate arrays to store results
- def compute(self, step, xs, ys, plot='False'): compute number fluctuations

<|skeleton|>
class NumberFluctuation:

    def __init__(self, nsteps, natoms, line):
        """initialize: allocate arrays to store results"""
        <|body_0|>

    def compute(self, step, xs, ys, plot='False'):
        """compute number fluctuations"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.dn = np.zeros((nsteps, nfrac))
        self.nav = float(natoms) / frac ** 2
        return
<|end_body_0|>

<|body_start_1|>
        for j in range(nfrac):
            hist, xedges, yedges = np.histogram2d(xs, ys, bins=frac[j], range=[[0, 1], [0, 1]])
            std = np.std(hist)
            self.dn[step, j] = std
        if plot == 'True':
            plt.loglog(self.nav, self.dn[step], marker='o')
            plt.show()
            plt.close()
        return
<|end_body_1|>

Solution:
class NumberFluctuation:
    def __init__(self, nsteps, natoms, line):
        """initialize: allocate arrays to store results"""
        self.dn = np.zeros((nsteps, nfrac))
        self.nav = float(natoms) / frac ** 2
        return

    def compute(self, step, xs, ys, plot='False'):
        """compute number fluctuations"""
        for j in range(nfrac):
            hist, xedges, yedges = np.histogram2d(xs, ys, bins=frac[j], range=[[0, 1], [0, 1]])
            std = np.std(hist)
            self.dn[step, j] = std
        if plot == 'True':
            plt.loglog(self.nav, self.dn[step], marker='o')
            plt.show()
            plt.close()
        return


insert

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

Class description:
Implement the Database class.

Method signatures and docstrings:
- def __init__(self, configuration): :param configuration: :param logger:
- def create_store(self, collection_name): this method create a collection in mongodb database :param collection_name: the collection name
- def insert(self, collection_name, item): :param collection_name: :param item: :return :generated for keys of inserted elements if success else false
- def find_by_video_id(self, collection_name, video_id, projection={}): :param collection_name: :param video_id: :param kvargs: {"key":val,...}possible key="comment","videoId","created_at","author", "lang", "likes","_id" possible val:1 to fetch,0 to ignore :return:

Implement the Python class `Database` described below.

Class description:
Implement the Database class.

Method signatures and docstrings:
- def __init__(self, configuration): :param configuration: :param logger:
- def create_store(self, collection_name): this method create a collection in mongodb database :param collection_name: the collection name
- def insert(self, collection_name, item): :param collection_name: :param item: :return :generated for keys of inserted elements if success else false
- def find_by_video_id(self, collection_name, video_id, projection={}): :param collection_name: :param video_id: :param kvargs: {"key":val,...}possible key="comment","videoId","created_at","author", "lang", "likes","_id" possible val:1 to fetch,0 to ignore :return:

<|skeleton|>
class Database:

    def __init__(self, configuration):
        """:param configuration: :param logger:"""
        <|body_0|>

    def create_store(self, collection_name):
        """this method create a collection in mongodb database :param collection_name: the collection name"""
        <|body_1|>

    def insert(self, collection_name, item):
        """:param collection_name: :param item: :return :generated for keys of inserted elements if success else false"""
        <|body_2|>

    def find_by_video_id(self, collection_name, video_id, projection={}):
        """:param collection_name: :param video_id: :param kvargs: {"key":val,...}possible key="comment","videoId","created_at","author", "lang", "likes","_id" possible val:1 to fetch,0 to ignore :return:"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        try:
            self._client = mongo.MongoClient(configuration['hostname'], configuration['port'])
            self._db = self._client[configuration['bdname']]
        except mongo.errors.PyMongoError:
            raise Exception('Error initializing database')
<|end_body_0|>

<|body_start_1|>
        try:
            mongo.collection.Collection(self._db, collection_name, create=True)
        except mongo.errors.PyMongoError:
            raise Exception('collection already exist')
<|end_body_1|>

<|body_start_2|>
        try:
            return self._db[collection_name].insert_one(item)
        except mongo.errors.PyMongoError:
            raise Exception('failed to insert data')
<|end_body_2|>

<|body_start_3|>
        try:
            return self._db[collection_name].find({'videoId': video_id}, projection)
        except mongo.errors.PyMongoError:
            raise Exception('failed to find video by id')
<|end_body_3|>

Solution:
class Database:
    def __init__(self, configuration):
        """:param configuration: :param logger:"""
        try:
            self._client = mongo.MongoClient(configuration['hostname'], configuration['port'])
            self._db = self._client[configuration['bdname']]
        except mongo.errors.PyMongoError:
            raise Exception('Error initializing database')

    def create_store(self, collection_name):
        """this method create a collection in mongodb database :param collection_name: the collection name"""
        try:
            mongo.collection.Collection(self._db, collection_name, create=True)
        except mongo.errors.PyMongoError:
            raise Exception('collection already exist')

    def insert(self, collection_name, item):
        """:param collection_name: :param item: :return :generated for keys of inserted elements if success else false"""
        try:
            return self._db[collection_name].insert_one(item)
        except mongo.errors.PyMongoError:
            raise Exception('failed to insert data')

    def find_by_video_id(self, collection_name, video_id, projection={}):
        """:param collection_name: :param video_id: :param kvargs: {"key":val,...}possible key="comment","videoId","created_at","author", "lang", "likes","_id" possible val:1 to fetch,0 to ignore :return:"""
        try:
            return self._db[collection_name].find({'videoId': video_id}, projection)
        except mongo.errors.PyMongoError:
            raise Exception('failed to find video by id')


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isAnagram1(self, s, t): :type s: str :type t: str :rtype: bool
- def isAnagram2(self, s, t): :type s: str :type t: str :rtype: bool

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isAnagram1(self, s, t): :type s: str :type t: str :rtype: bool
- def isAnagram2(self, s, t): :type s: str :type t: str :rtype: bool

<|skeleton|>
class Solution:

    def isAnagram1(self, s, t):
        """:type s: str :type t: str :rtype: bool"""
        <|body_0|>

    def isAnagram2(self, s, t):
        """:type s: str :type t: str :rtype: bool"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if len(s) != len(t):
            return False
        d = {}
        for c in s:
            if c in d:
                d[c] += 1
            else:
                d[c] = 1
        for c in t:
            if c not in d:
                return False
            else:
                d[c] -= 1
            if d[c] < 0:
                return False
        return True
<|end_body_0|>

<|body_start_1|>
        if len(s) != len(t):
            return False
        d = dict.fromkeys(string.ascii_lowercase, 0)
        for e in s:
            if e in d:
                d[e] += 1
        for i in t:
            if i in d:
                d[i] -= 1
        for ele in d:
            if d[ele] != 0:
                return False
        return True
<|end_body_1|>

Solution:
class Solution:
    def isAnagram1(self, s, t):
        """:type s: str :type t: str :rtype: bool"""
        if len(s) != len(t):
            return False
        d = {}
        for c in s:
            if c in d:
                d[c] += 1
            else:
                d[c] = 1
        for c in t:
            if c not in d:
                return False
            else:
                d[c] -= 1
            if d[c] < 0:
                return False
        return True

    def isAnagram2(self, s, t):
        """:type s: str :type t: str :rtype: bool"""
        if len(s) != len(t):
            return False
        d = dict.fromkeys(string.ascii_lowercase, 0)
        for e in s:
            if e in d:
                d[e] += 1
        for i in t:
            if i in d:
                d[i] -= 1
        for ele in d:
            if d[ele] != 0:
                return False
        return True


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rob(self, root): :type root: TreeNode :rtype: int
- def rob(self, root): :type root: TreeNode :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rob(self, root): :type root: TreeNode :rtype: int
- def rob(self, root): :type root: TreeNode :rtype: int

<|skeleton|>
class Solution:

    def rob(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_0|>

    def rob(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        rob_record = {}

        def rob_tree(root):
            if not root:
                return 0
            if root in rob_record:
                return rob_record[root]
            result = max(rob_child(root), root.val + rob_child(root.left) + rob_child(root.right))
            rob_record[root] = result
            return result

        def rob_child(node):
            if not node:
                return 0
            return rob_tree(node.left) + rob_tree(node.right)
        return rob_tree(root)
<|end_body_0|>

<|body_start_1|>
        def rob3(root):
            if not root:
                return (0, 0, 0)
            l, ll, lr = rob3(root.left)
            r, rl, rr = rob3(root.right)
            return (max(root.val + ll + lr + rl + rr, l + r), l, r)
        return rob3(root)[0]
<|end_body_1|>

Solution:
class Solution:
    def rob(self, root):
        """:type root: TreeNode :rtype: int"""
        rob_record = {}

        def rob_tree(root):
            if not root:
                return 0
            if root in rob_record:
                return rob_record[root]
            result = max(rob_child(root), root.val + rob_child(root.left) + rob_child(root.right))
            rob_record[root] = result
            return result

        def rob_child(node):
            if not node:
                return 0
            return rob_tree(node.left) + rob_tree(node.right)
        return rob_tree(root)

    def rob(self, root):
        """:type root: TreeNode :rtype: int"""
        def rob3(root):
            if not root:
                return (0, 0, 0)
            l, ll, lr = rob3(root.left)
            r, rl, rr = rob3(root.right)
            return (max(root.val + ll + lr + rl + rr, l + r), l, r)
        return rob3(root)[0]


forward

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

Class description:
epresents an LSTM unit

Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Wf, Wu, Wc, Wo, Wy, bf, bu, bc, bo, by weights and biases @Wf and bf are for the forget gate @Wu and bu are for the update gate @Wc and bc are for the intermediate cell state @Wo and bo are for the output gate @Wy and by are for the outputs
- def forward(self, h_prev, c_prev, x_t): performs forward propagation for one time step @h_prev: np.ndarray shape(m, h) containing the previous hidden state @x_t: np.ndarray of shape(m, i) containing the data input for the cell @m: batche size for the data Retrun: h_next, c_next, y @h_next is the next hidden state @y: output of the cell

Implement the Python class `LSTMCell` described below.

Class description:
epresents an LSTM unit

Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Wf, Wu, Wc, Wo, Wy, bf, bu, bc, bo, by weights and biases @Wf and bf are for the forget gate @Wu and bu are for the update gate @Wc and bc are for the intermediate cell state @Wo and bo are for the output gate @Wy and by are for the outputs
- def forward(self, h_prev, c_prev, x_t): performs forward propagation for one time step @h_prev: np.ndarray shape(m, h) containing the previous hidden state @x_t: np.ndarray of shape(m, i) containing the data input for the cell @m: batche size for the data Retrun: h_next, c_next, y @h_next is the next hidden state @y: output of the cell

<|skeleton|>
class LSTMCell:
    """epresents an LSTM unit"""

    def __init__(self, i, h, o):
        """Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Wf, Wu, Wc, Wo, Wy, bf, bu, bc, bo, by weights and biases @Wf and bf are for the forget gate @Wu and bu are for the update gate @Wc and bc are for the intermediate cell state @Wo and bo are for the output gate @Wy and by are for the outputs"""
        <|body_0|>

    def forward(self, h_prev, c_prev, x_t):
        """performs forward propagation for one time step @h_prev: np.ndarray shape(m, h) containing the previous hidden state @x_t: np.ndarray of shape(m, i) containing the data input for the cell @m: batche size for the data Retrun: h_next, c_next, y @h_next is the next hidden state @y: output of the cell"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.Wf = np.random.normal(size=(h + i, h))
        self.Wu = np.random.normal(size=(h + i, h))
        self.Wc = np.random.normal(size=(h + i, h))
        self.Wo = np.random.normal(size=(h + i, h))
        self.Wy = np.random.normal(size=(h, o))
        self.bo = np.zeros((1, h))
        self.bc = np.zeros((1, h))
        self.bu = np.zeros((1, h))
        self.bf = np.zeros((1, h))
        self.by = np.zeros((1, o))
<|end_body_0|>

<|body_start_1|>
        con = np.concatenate((h_prev, x_t), axis=1)
        x = np.matmul(con, self.Wf) + self.bf
        forget = 1 / (1 + np.exp(-x))
        x = np.matmul(con, self.Wu) + self.bu
        update = 1 / (1 + np.exp(-x))
        x = np.matmul(con, self.Wo) + self.bo
        out = 1 / (1 + np.exp(-x))
        c = np.tanh(np.matmul(con, self.Wc) + self.bc)
        c_next = forget * c_prev + c * update
        h_next = out * np.tanh(c_next)
        soft = np.matmul(h_next, self.Wy) + self.by
        x_max = np.max(soft, axis=-1, keepdims=True)
        x_exp = np.exp(soft - x_max)
        y = x_exp / x_exp.sum(axis=-1, keepdims=True)
        return (h_next, c_next, y)
<|end_body_1|>

Solution:
class LSTMCell:
    """epresents an LSTM unit"""

    def __init__(self, i, h, o):
        """Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Wf, Wu, Wc, Wo, Wy, bf, bu, bc, bo, by weights and biases @Wf and bf are for the forget gate @Wu and bu are for the update gate @Wc and bc are for the intermediate cell state @Wo and bo are for the output gate @Wy and by are for the outputs"""
        self.Wf = np.random.normal(size=(h + i, h))
        self.Wu = np.random.normal(size=(h + i, h))
        self.Wc = np.random.normal(size=(h + i, h))
        self.Wo = np.random.normal(size=(h + i, h))
        self.Wy = np.random.normal(size=(h, o))
        self.bo = np.zeros((1, h))
        self.bc = np.zeros((1, h))
        self.bu = np.zeros((1, h))
        self.bf = np.zeros((1, h))
        self.by = np.zeros((1, o))

    def forward(self, h_prev, c_prev, x_t):
        """performs forward propagation for one time step @h_prev: np.ndarray shape(m, h) containing the previous hidden state @x_t: np.ndarray of shape(m, i) containing the data input for the cell @m: batche size for the data Retrun: h_next, c_next, y @h_next is the next hidden state @y: output of the cell"""
        con = np.concatenate((h_prev, x_t), axis=1)
        x = np.matmul(con, self.Wf) + self.bf
        forget = 1 / (1 + np.exp(-x))
        x = np.matmul(con, self.Wu) + self.bu
        update = 1 / (1 + np.exp(-x))
        x = np.matmul(con, self.Wo) + self.bo
        out = 1 / (1 + np.exp(-x))
        c = np.tanh(np.matmul(con, self.Wc) + self.bc)
        c_next = forget * c_prev + c * update
        h_next = out * np.tanh(c_next)
        soft = np.matmul(h_next, self.Wy) + self.by
        x_max = np.max(soft, axis=-1, keepdims=True)
        x_exp = np.exp(soft - x_max)
        y = x_exp / x_exp.sum(axis=-1, keepdims=True)
        return (h_next, c_next, y)


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

Class description:
A node that contains a number. Outputs a single column containing the number.

Method signatures and docstrings:
- def __init__(self, xml_node, col_name, *, units=None): Parameters ---------- xml_node : `xml.etree.ElementTree` the xml node that provides the raw data for this DustNode col_name : str the name of the column associated with this item units : `~astropy.units.Unit` the units associated with this item
- def __str__(self): Return a string representation of the item.

Implement the Python class `NumberNode` described below.

Class description:
A node that contains a number. Outputs a single column containing the number.

Method signatures and docstrings:
- def __init__(self, xml_node, col_name, *, units=None): Parameters ---------- xml_node : `xml.etree.ElementTree` the xml node that provides the raw data for this DustNode col_name : str the name of the column associated with this item units : `~astropy.units.Unit` the units associated with this item
- def __str__(self): Return a string representation of the item.

<|skeleton|>
class NumberNode:
    """A node that contains a number. Outputs a single column containing the number."""

    def __init__(self, xml_node, col_name, *, units=None):
        """Parameters ---------- xml_node : `xml.etree.ElementTree` the xml node that provides the raw data for this DustNode col_name : str the name of the column associated with this item units : `~astropy.units.Unit` the units associated with this item"""
        <|body_0|>

    def __str__(self):
        """Return a string representation of the item."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        BaseDustNode.__init__(self, xml_node)
        self._value = utils.parse_number(xml_node.text)
        self._columns = [Column(name=col_name, unit=units)]
<|end_body_0|>

<|body_start_1|>
        base_string = BaseDustNode.__str__(self)
        string = '[NumberNode: ' + base_string + ', value: ' + str(self._value) + ']'
        return string
<|end_body_1|>

Solution:
class NumberNode:
    """A node that contains a number. Outputs a single column containing the number."""

    def __init__(self, xml_node, col_name, *, units=None):
        """Parameters ---------- xml_node : `xml.etree.ElementTree` the xml node that provides the raw data for this DustNode col_name : str the name of the column associated with this item units : `~astropy.units.Unit` the units associated with this item"""
        BaseDustNode.__init__(self, xml_node)
        self._value = utils.parse_number(xml_node.text)
        self._columns = [Column(name=col_name, unit=units)]

    def __str__(self):
        """Return a string representation of the item."""
        base_string = BaseDustNode.__str__(self)
        string = '[NumberNode: ' + base_string + ', value: ' + str(self._value) + ']'
        return string


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

Class description:
Lstm model for seq classification task.

Method signatures and docstrings:
- def __init__(self, vocab_size, num_class, emb_dim=512, hidden_size=512, n_lstm_layer=3, is_bidirectory=True, padding_idx=0, epsilon=1e-05, dropout_rate=0.1): Init model Args: vocab_size (int): vocab size. num_class (int): num of classes. emb_dim (int, optional): embedding dimmension. Defaults to 512. hidden_size (int, optional): hidden size. Defaults to 512. n_lstm_layer (int, optional): number of lstm layer. Defaults to 3. is_bidirectory (bool, optional): use bidirect lstm. Defaults to True. padding_idx (int, optional): padding index. Defaults to 0. epsilon (float, optional): epsilon. Defaults to 1e-5. dropout_rate (float, optional): dropout rate. Defaults to 0.1.
- def _prepare_emb(self, tokens): prepare emb
- def _seq_classification_task(self, lstm_output): calc seq class loss
- def forward(self, tokens, seq_lens): model forward

Implement the Python class `LstmSeqClassificationModel` described below.

Class description:
Lstm model for seq classification task.

Method signatures and docstrings:
- def __init__(self, vocab_size, num_class, emb_dim=512, hidden_size=512, n_lstm_layer=3, is_bidirectory=True, padding_idx=0, epsilon=1e-05, dropout_rate=0.1): Init model Args: vocab_size (int): vocab size. num_class (int): num of classes. emb_dim (int, optional): embedding dimmension. Defaults to 512. hidden_size (int, optional): hidden size. Defaults to 512. n_lstm_layer (int, optional): number of lstm layer. Defaults to 3. is_bidirectory (bool, optional): use bidirect lstm. Defaults to True. padding_idx (int, optional): padding index. Defaults to 0. epsilon (float, optional): epsilon. Defaults to 1e-5. dropout_rate (float, optional): dropout rate. Defaults to 0.1.
- def _prepare_emb(self, tokens): prepare emb
- def _seq_classification_task(self, lstm_output): calc seq class loss
- def forward(self, tokens, seq_lens): model forward

<|skeleton|>
class LstmSeqClassificationModel:
    """Lstm model for seq classification task."""

    def __init__(self, vocab_size, num_class, emb_dim=512, hidden_size=512, n_lstm_layer=3, is_bidirectory=True, padding_idx=0, epsilon=1e-05, dropout_rate=0.1):
        """Init model Args: vocab_size (int): vocab size. num_class (int): num of classes. emb_dim (int, optional): embedding dimmension. Defaults to 512. hidden_size (int, optional): hidden size. Defaults to 512. n_lstm_layer (int, optional): number of lstm layer. Defaults to 3. is_bidirectory (bool, optional): use bidirect lstm. Defaults to True. padding_idx (int, optional): padding index. Defaults to 0. epsilon (float, optional): epsilon. Defaults to 1e-5. dropout_rate (float, optional): dropout rate. Defaults to 0.1."""
        <|body_0|>

    def _prepare_emb(self, tokens):
        """prepare emb"""
        <|body_1|>

    def _seq_classification_task(self, lstm_output):
        """calc seq class loss"""
        <|body_2|>

    def forward(self, tokens, seq_lens):
        """model forward"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super(LstmSeqClassificationModel, self).__init__()
        self.padding_idx = padding_idx
        self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
        self.layer_norm = nn.LayerNorm(normalized_shape=emb_dim, epsilon=epsilon)
        self.dropout = nn.Dropout(p=dropout_rate)
        direction = 'bidirectional' if is_bidirectory else 'forward'
        self.lstm_encoder = nn.LSTM(emb_dim, hidden_size, num_layers=n_lstm_layer, direction=direction)
        in_channels = hidden_size * 2 if is_bidirectory else hidden_size
        self.conv_encoder = nn.Conv1D(in_channels=in_channels, out_channels=hidden_size, kernel_size=5, padding=2)
        self.output_layer = nn.Conv1D(in_channels=hidden_size, out_channels=num_class, kernel_size=3, padding=1)
<|end_body_0|>

<|body_start_1|>
        embedded_text = self.embedder(tokens)
        embedded_text = self.layer_norm(embedded_text)
        embedded_text = self.dropout(embedded_text)
        return embedded_text
<|end_body_1|>

<|body_start_2|>
        conv_out = self.conv_encoder(lstm_output)
        conv_out = F.relu(conv_out)
        logits = self.output_layer(conv_out).transpose(perm=(0, 2, 1))
        return logits
<|end_body_2|>

<|body_start_3|>
        embedded_text = self._prepare_emb(tokens)
        lstm_output, (last_hidden, last_cell) = self.lstm_encoder(embedded_text)
        lstm_output = lstm_output.transpose(perm=(0, 2, 1))
        logits = self._seq_classification_task(lstm_output)
        return logits
<|end_body_3|>

Solution:
class LstmSeqClassificationModel:
    """Lstm model for seq classification task."""

    def __init__(self, vocab_size, num_class, emb_dim=512, hidden_size=512, n_lstm_layer=3, is_bidirectory=True, padding_idx=0, epsilon=1e-05, dropout_rate=0.1):
        """Init model Args: vocab_size (int): vocab size. num_class (int): num of classes. emb_dim (int, optional): embedding dimmension. Defaults to 512. hidden_size (int, optional): hidden size. Defaults to 512. n_lstm_layer (int, optional): number of lstm layer. Defaults to 3. is_bidirectory (bool, optional): use bidirect lstm. Defaults to True. padding_idx (int, optional): padding index. Defaults to 0. epsilon (float, optional): epsilon. Defaults to 1e-5. dropout_rate (float, optional): dropout rate. Defaults to 0.1."""
        super(LstmSeqClassificationModel, self).__init__()
        self.padding_idx = padding_idx
        self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
        self.layer_norm = nn.LayerNorm(normalized_shape=emb_dim, epsilon=epsilon)
        self.dropout = nn.Dropout(p=dropout_rate)
        direction = 'bidirectional' if is_bidirectory else 'forward'
        self.lstm_encoder = nn.LSTM(emb_dim, hidden_size, num_layers=n_lstm_layer, direction=direction)
        in_channels = hidden_size * 2 if is_bidirectory else hidden_size
        self.conv_encoder = nn.Conv1D(in_channels=in_channels, out_channels=hidden_size, kernel_size=5, padding=2)
        self.output_layer = nn.Conv1D(in_channels=hidden_size, out_channels=num_class, kernel_size=3, padding=1)

    def _prepare_emb(self, tokens):
        """prepare emb"""
        embedded_text = self.embedder(tokens)
        embedded_text = self.layer_norm(embedded_text)
        embedded_text = self.dropout(embedded_text)
        return embedded_text

    def _seq_classification_task(self, lstm_output):
        """calc seq class loss"""
        conv_out = self.conv_encoder(lstm_output)
        conv_out = F.relu(conv_out)
        logits = self.output_layer(conv_out).transpose(perm=(0, 2, 1))
        return logits

    def forward(self, tokens, seq_lens):
        """model forward"""
        embedded_text = self._prepare_emb(tokens)
        lstm_output, (last_hidden, last_cell) = self.lstm_encoder(embedded_text)
        lstm_output = lstm_output.transpose(perm=(0, 2, 1))
        logits = self._seq_classification_task(lstm_output)
        return logits


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

Class description:
A base parser for all motif csv files. This is callable and will produce a generator of dictonaries. If the row has a 'name' property we will yield the name and dictonary, otherwise we just yield the dictonary. This will also convert the field values specified by convert property.

Method signatures and docstrings:
- def __convert__(self, entry): Converts the values as requested. :param dict entry: The dictonary to convert.
- def __call__(self, directory): Parse the file in the given directory. :param str directory: The directory to get the file in. :yields: Each row in the file, after converting.

Implement the Python class `BaseParser` described below.

Class description:
A base parser for all motif csv files. This is callable and will produce a generator of dictonaries. If the row has a 'name' property we will yield the name and dictonary, otherwise we just yield the dictonary. This will also convert the field values specified by convert property.

Method signatures and docstrings:
- def __convert__(self, entry): Converts the values as requested. :param dict entry: The dictonary to convert.
- def __call__(self, directory): Parse the file in the given directory. :param str directory: The directory to get the file in. :yields: Each row in the file, after converting.

<|skeleton|>
class BaseParser:
    """A base parser for all motif csv files. This is callable and will produce a generator of dictonaries. If the row has a 'name' property we will yield the name and dictonary, otherwise we just yield the dictonary. This will also convert the field values specified by convert property."""

    def __convert__(self, entry):
        """Converts the values as requested. :param dict entry: The dictonary to convert."""
        <|body_0|>

    def __call__(self, directory):
        """Parse the file in the given directory. :param str directory: The directory to get the file in. :yields: Each row in the file, after converting."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = {}
        for key, value in entry.items():
            converter = self.convert.get(key, str)
            data[key] = converter(value)
        return data
<|end_body_0|>

<|body_start_1|>
        with open(os.path.join(directory, self.filename)) as raw:
            reader = csv.DictReader(raw, fieldnames=self.header)
            seen = False
            for row in reader:
                converted = self.__convert__(row)
                if 'name' in converted:
                    name = converted.pop('name')
                    yield (name, converted)
                else:
                    yield converted
                seen = True
            if not seen:
                raise core.InvalidState('No rows for %s' % self.filename)
<|end_body_1|>

Solution:
class BaseParser:
    """A base parser for all motif csv files. This is callable and will produce a generator of dictonaries. If the row has a 'name' property we will yield the name and dictonary, otherwise we just yield the dictonary. This will also convert the field values specified by convert property."""

    def __convert__(self, entry):
        """Converts the values as requested. :param dict entry: The dictonary to convert."""
        data = {}
        for key, value in entry.items():
            converter = self.convert.get(key, str)
            data[key] = converter(value)
        return data

    def __call__(self, directory):
        """Parse the file in the given directory. :param str directory: The directory to get the file in. :yields: Each row in the file, after converting."""
        with open(os.path.join(directory, self.filename)) as raw:
            reader = csv.DictReader(raw, fieldnames=self.header)
            seen = False
            for row in reader:
                converted = self.__convert__(row)
                if 'name' in converted:
                    name = converted.pop('name')
                    yield (name, converted)
                else:
                    yield converted
                seen = True
            if not seen:
                raise core.InvalidState('No rows for %s' % self.filename)


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

Class description:
Defines a base Sonarr entity.

Method signatures and docstrings:
- def __init__(self, *, sonarr: SonarrClient, host_config: PyArrHostConfiguration, system_status: SystemStatus, entry_id: str, device_id: str) -> None: Initialize the Sonarr entity.
- def device_info(self) -> DeviceInfo | None: Return device information about the application.

Implement the Python class `SonarrEntity` described below.

Class description:
Defines a base Sonarr entity.

Method signatures and docstrings:
- def __init__(self, *, sonarr: SonarrClient, host_config: PyArrHostConfiguration, system_status: SystemStatus, entry_id: str, device_id: str) -> None: Initialize the Sonarr entity.
- def device_info(self) -> DeviceInfo | None: Return device information about the application.

<|skeleton|>
class SonarrEntity:
    """Defines a base Sonarr entity."""

    def __init__(self, *, sonarr: SonarrClient, host_config: PyArrHostConfiguration, system_status: SystemStatus, entry_id: str, device_id: str) -> None:
        """Initialize the Sonarr entity."""
        <|body_0|>

    def device_info(self) -> DeviceInfo | None:
        """Return device information about the application."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self._entry_id = entry_id
        self._device_id = device_id
        self.sonarr = sonarr
        self.host_config = host_config
        self.system_status = system_status
<|end_body_0|>

<|body_start_1|>
        if self._device_id is None:
            return None
        return DeviceInfo(identifiers={(DOMAIN, self._device_id)}, name='Activity Sensor', manufacturer='Sonarr', sw_version=self.system_status.version, entry_type=DeviceEntryType.SERVICE, configuration_url=self.host_config.base_url)
<|end_body_1|>

Solution:
class SonarrEntity:
    """Defines a base Sonarr entity."""

    def __init__(self, *, sonarr: SonarrClient, host_config: PyArrHostConfiguration, system_status: SystemStatus, entry_id: str, device_id: str) -> None:
        """Initialize the Sonarr entity."""
        self._entry_id = entry_id
        self._device_id = device_id
        self.sonarr = sonarr
        self.host_config = host_config
        self.system_status = system_status

    def device_info(self) -> DeviceInfo | None:
        """Return device information about the application."""
        if self._device_id is None:
            return None
        return DeviceInfo(identifiers={(DOMAIN, self._device_id)}, name='Activity Sensor', manufacturer='Sonarr', sw_version=self.system_status.version, entry_type=DeviceEntryType.SERVICE, configuration_url=self.host_config.base_url)


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

Class description:
Implement the GetServiceArea class.

Method signatures and docstrings:
- def test_success(self): Test Success
- def test_failure(self): Test Failure

Implement the Python class `GetServiceArea` described below.

Class description:
Implement the GetServiceArea class.

Method signatures and docstrings:
- def test_success(self): Test Success
- def test_failure(self): Test Failure

<|skeleton|>
class GetServiceArea:

    def test_success(self):
        """Test Success"""
        <|body_0|>

    def test_failure(self):
        """Test Failure"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        r = self.client.put('/api/company/', LOGIN_COMPANY_OBJECT_SUCCESS, content_type='application/json')
        token = r.data['data']['access_token']
        header = {'HTTP_AUTHORIZATION': 'Bearer {token}'.format(token=token)}
        r = self.client.get('/api/my-service-area/efcb0080-1ce9-47a0-8299-15c126b84dd4/', content_type='application/json', **header)
        self.assertTrue(r.data['success'])
        self.assertEqual(r.status_code, status.HTTP_200_OK)
<|end_body_0|>

<|body_start_1|>
        r = self.client.put('/api/company/', LOGIN_COMPANY_OBJECT_SUCCESS, content_type='application/json')
        token = r.data['data']['access_token']
        header = {'HTTP_AUTHORIZATION': 'Bearer {token}'.format(token=token)}
        r = self.client.get('/api/my-service-area/efcb0080-1ce9-47a0-8299-15c126b84d32/', content_type='application/json', **header)
        self.assertFalse(r.data['success'])
        self.assertEqual(r.status_code, status.HTTP_204_NO_CONTENT)
<|end_body_1|>

Solution:
class GetServiceArea:
    def test_success(self):
        """Test Success"""
        r = self.client.put('/api/company/', LOGIN_COMPANY_OBJECT_SUCCESS, content_type='application/json')
        token = r.data['data']['access_token']
        header = {'HTTP_AUTHORIZATION': 'Bearer {token}'.format(token=token)}
        r = self.client.get('/api/my-service-area/efcb0080-1ce9-47a0-8299-15c126b84dd4/', content_type='application/json', **header)
        self.assertTrue(r.data['success'])
        self.assertEqual(r.status_code, status.HTTP_200_OK)

    def test_failure(self):
        """Test Failure"""
        r = self.client.put('/api/company/', LOGIN_COMPANY_OBJECT_SUCCESS, content_type='application/json')
        token = r.data['data']['access_token']
        header = {'HTTP_AUTHORIZATION': 'Bearer {token}'.format(token=token)}
        r = self.client.get('/api/my-service-area/efcb0080-1ce9-47a0-8299-15c126b84d32/', content_type='application/json', **header)
        self.assertFalse(r.data['success'])
        self.assertEqual(r.status_code, status.HTTP_204_NO_CONTENT)