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

Class description:
Implement the DefaultXGBoostAlgoModeInferenceHandler class.

Method signatures and docstrings:
- def default_model_fn(self, model_dir): Load a model. For XGBoost Framework, a default function to load a model is not provided. Users should provide customized model_fn() in script. Args: model_dir: a directory where model is saved. Returns: A XGBoost model. XGBoost model format type.
- def default_input_fn(self, input_data, input_content_type): Take request data and de-serializes the data into an object for prediction. When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server, the model server receives two pieces of information: - The request Content-Type, for example "application/json" - The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size. The input_fn is responsible to take the request data and pre-process it before prediction. Args: input_data (obj): the request data. input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF. Returns: (obj): data ready for prediction. For XGBoost, this defaults to DMatrix.
- def default_predict_fn(self, data, model): A default predict_fn for XGBooost Framework. Calls a model on data deserialized in input_fn. Args: data: input data (DMatrix) for prediction deserialized by input_fn and data content type model: XGBoost model loaded in memory by model_fn, and xgboost model format Returns: a prediction
- def default_output_fn(self, prediction, accept): Return encoded prediction for the response. Args: prediction (obj): prediction returned by predict_fn . accept (str): accept content-type expected by the client. Returns: encoded response for MMS to return to client

Implement the Python class `DefaultXGBoostAlgoModeInferenceHandler` described below.

Class description:
Implement the DefaultXGBoostAlgoModeInferenceHandler class.

Method signatures and docstrings:
- def default_model_fn(self, model_dir): Load a model. For XGBoost Framework, a default function to load a model is not provided. Users should provide customized model_fn() in script. Args: model_dir: a directory where model is saved. Returns: A XGBoost model. XGBoost model format type.
- def default_input_fn(self, input_data, input_content_type): Take request data and de-serializes the data into an object for prediction. When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server, the model server receives two pieces of information: - The request Content-Type, for example "application/json" - The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size. The input_fn is responsible to take the request data and pre-process it before prediction. Args: input_data (obj): the request data. input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF. Returns: (obj): data ready for prediction. For XGBoost, this defaults to DMatrix.
- def default_predict_fn(self, data, model): A default predict_fn for XGBooost Framework. Calls a model on data deserialized in input_fn. Args: data: input data (DMatrix) for prediction deserialized by input_fn and data content type model: XGBoost model loaded in memory by model_fn, and xgboost model format Returns: a prediction
- def default_output_fn(self, prediction, accept): Return encoded prediction for the response. Args: prediction (obj): prediction returned by predict_fn . accept (str): accept content-type expected by the client. Returns: encoded response for MMS to return to client

<|skeleton|>
class DefaultXGBoostAlgoModeInferenceHandler:

    def default_model_fn(self, model_dir):
        """Load a model. For XGBoost Framework, a default function to load a model is not provided. Users should provide customized model_fn() in script. Args: model_dir: a directory where model is saved. Returns: A XGBoost model. XGBoost model format type."""
        <|body_0|>

    def default_input_fn(self, input_data, input_content_type):
        """Take request data and de-serializes the data into an object for prediction. When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server, the model server receives two pieces of information: - The request Content-Type, for example "application/json" - The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size. The input_fn is responsible to take the request data and pre-process it before prediction. Args: input_data (obj): the request data. input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF. Returns: (obj): data ready for prediction. For XGBoost, this defaults to DMatrix."""
        <|body_1|>

    def default_predict_fn(self, data, model):
        """A default predict_fn for XGBooost Framework. Calls a model on data deserialized in input_fn. Args: data: input data (DMatrix) for prediction deserialized by input_fn and data content type model: XGBoost model loaded in memory by model_fn, and xgboost model format Returns: a prediction"""
        <|body_2|>

    def default_output_fn(self, prediction, accept):
        """Return encoded prediction for the response. Args: prediction (obj): prediction returned by predict_fn . accept (str): accept content-type expected by the client. Returns: encoded response for MMS to return to client"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        try:
            booster, format = serve_utils.get_loaded_booster(model_dir, serve_utils.is_ensemble_enabled())
        except Exception as e:
            raise ModelLoadInferenceError('Unable to load model: {}'.format(str(e)))
        return (booster, format)
<|end_body_0|>

<|body_start_1|>
        if len(input_data) == 0:
            raise NoContentInferenceError()
        dtest, content_type = serve_utils.parse_content_data(input_data, input_content_type)
        return (dtest, content_type)
<|end_body_1|>

<|body_start_2|>
        booster, model_format = model
        dtest, content_type = data
        try:
            return serve_utils.predict(booster, model_format, dtest, content_type)
        except Exception as e:
            raise BadRequestInferenceError(str(e))
<|end_body_2|>

<|body_start_3|>
        accept_type = accept.lower()
        try:
            if accept_type == content_types.CSV or accept_type == 'csv':
                if SAGEMAKER_BATCH:
                    return_data = '\n'.join(map(str, prediction.tolist())) + '\n'
                else:
                    return_data = ','.join(map(str, prediction.tolist()))
                encoded_prediction = return_data.encode('utf-8')
            elif accept_type == content_types.JSON or accept_type == 'json':
                encoded_prediction = encoder.encode(prediction, accept_type)
            else:
                raise ValueError("{} is not an accepted Accept type. Please choose one of the following: ['{}', '{}'].".format(accept, content_types.CSV, content_types.JSON))
        except Exception as e:
            raise UnsupportedMediaTypeInferenceError('Encoding to accept type {} failed with exception: {}'.format(accept, e))
        return encoded_prediction
<|end_body_3|>

Solution:
class DefaultXGBoostAlgoModeInferenceHandler:
    def default_model_fn(self, model_dir):
        """Load a model. For XGBoost Framework, a default function to load a model is not provided. Users should provide customized model_fn() in script. Args: model_dir: a directory where model is saved. Returns: A XGBoost model. XGBoost model format type."""
        try:
            booster, format = serve_utils.get_loaded_booster(model_dir, serve_utils.is_ensemble_enabled())
        except Exception as e:
            raise ModelLoadInferenceError('Unable to load model: {}'.format(str(e)))
        return (booster, format)

    def default_input_fn(self, input_data, input_content_type):
        """Take request data and de-serializes the data into an object for prediction. When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server, the model server receives two pieces of information: - The request Content-Type, for example "application/json" - The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size. The input_fn is responsible to take the request data and pre-process it before prediction. Args: input_data (obj): the request data. input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF. Returns: (obj): data ready for prediction. For XGBoost, this defaults to DMatrix."""
        if len(input_data) == 0:
            raise NoContentInferenceError()
        dtest, content_type = serve_utils.parse_content_data(input_data, input_content_type)
        return (dtest, content_type)

    def default_predict_fn(self, data, model):
        """A default predict_fn for XGBooost Framework. Calls a model on data deserialized in input_fn. Args: data: input data (DMatrix) for prediction deserialized by input_fn and data content type model: XGBoost model loaded in memory by model_fn, and xgboost model format Returns: a prediction"""
        booster, model_format = model
        dtest, content_type = data
        try:
            return serve_utils.predict(booster, model_format, dtest, content_type)
        except Exception as e:
            raise BadRequestInferenceError(str(e))

    def default_output_fn(self, prediction, accept):
        """Return encoded prediction for the response. Args: prediction (obj): prediction returned by predict_fn . accept (str): accept content-type expected by the client. Returns: encoded response for MMS to return to client"""
        accept_type = accept.lower()
        try:
            if accept_type == content_types.CSV or accept_type == 'csv':
                if SAGEMAKER_BATCH:
                    return_data = '\n'.join(map(str, prediction.tolist())) + '\n'
                else:
                    return_data = ','.join(map(str, prediction.tolist()))
                encoded_prediction = return_data.encode('utf-8')
            elif accept_type == content_types.JSON or accept_type == 'json':
                encoded_prediction = encoder.encode(prediction, accept_type)
            else:
                raise ValueError("{} is not an accepted Accept type. Please choose one of the following: ['{}', '{}'].".format(accept, content_types.CSV, content_types.JSON))
        except Exception as e:
            raise UnsupportedMediaTypeInferenceError('Encoding to accept type {} failed with exception: {}'.format(accept, e))
        return encoded_prediction


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

Class description:
convolutional highway layer

Method signatures and docstrings:
- def __init__(self, num_filter, window_size, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='conv_highway'): initialize convolutional highway layer
- def __call__(self, input_data, input_mask): call convolutional highway layer

Implement the Python class `ConvHighway` described below.

Class description:
convolutional highway layer

Method signatures and docstrings:
- def __init__(self, num_filter, window_size, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='conv_highway'): initialize convolutional highway layer
- def __call__(self, input_data, input_mask): call convolutional highway layer

<|skeleton|>
class ConvHighway:
    """convolutional highway layer"""

    def __init__(self, num_filter, window_size, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='conv_highway'):
        """initialize convolutional highway layer"""
        <|body_0|>

    def __call__(self, input_data, input_mask):
        """call convolutional highway layer"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.num_filter = num_filter
        self.window_size = window_size
        self.activation = activation
        self.dropout = dropout
        self.regularizer = regularizer
        self.random_seed = random_seed
        self.trainable = trainable
        self.scope = scope
        self.device_spec = get_device_spec(default_gpu_id, num_gpus)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
            weight_initializer = create_variable_initializer('glorot_uniform', self.m_seed)
            bias_initializer = create_variable_initializer('zero')
            transform_activation = create_activation_function(self.activation)
            gate_activation = create_activation_function('sigmoid')
            self.transform_layer = tf.layers.Conv1D(filters=self.num_filter, kernel_size=window_size, strides=1, padding='SAME', activation=transform_activation, use_bias=True, kernel_initializer=weight_initializer, bias_initializer=bias_initializer, kernel_regularizer=self.regularizer, bias_regularizer=self.regularizer, trainable=trainable)
            self.gate_layer = tf.layers.Conv1D(filters=self.num_filter, kernel_size=window_size, strides=1, padding='SAME', activation=gate_activation, use_bias=True, kernel_initializer=weight_initializer, bias_initializer=bias_initializer, kernel_regularizer=self.regularizer, bias_regularizer=self.regularizer, trainable=trainable)
            self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus, default_gpu_id=default_gpu_id, random_seed=self.random_seed)
<|end_body_0|>

<|body_start_1|>
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
            transform, _ = self.dropout_layer(self.transform_layer(input_data), input_mask)
            gate = self.gate_layer(input_data)
            output_highway = transform * gate + input_data * (1 - gate)
            output_mask = input_mask
        return (output_highway, output_mask)
<|end_body_1|>

Solution:
class ConvHighway:
    """convolutional highway layer"""

    def __init__(self, num_filter, window_size, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='conv_highway'):
        """initialize convolutional highway layer"""
        self.num_filter = num_filter
        self.window_size = window_size
        self.activation = activation
        self.dropout = dropout
        self.regularizer = regularizer
        self.random_seed = random_seed
        self.trainable = trainable
        self.scope = scope
        self.device_spec = get_device_spec(default_gpu_id, num_gpus)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
            weight_initializer = create_variable_initializer('glorot_uniform', self.m_seed)
            bias_initializer = create_variable_initializer('zero')
            transform_activation = create_activation_function(self.activation)
            gate_activation = create_activation_function('sigmoid')
            self.transform_layer = tf.layers.Conv1D(filters=self.num_filter, kernel_size=window_size, strides=1, padding='SAME', activation=transform_activation, use_bias=True, kernel_initializer=weight_initializer, bias_initializer=bias_initializer, kernel_regularizer=self.regularizer, bias_regularizer=self.regularizer, trainable=trainable)
            self.gate_layer = tf.layers.Conv1D(filters=self.num_filter, kernel_size=window_size, strides=1, padding='SAME', activation=gate_activation, use_bias=True, kernel_initializer=weight_initializer, bias_initializer=bias_initializer, kernel_regularizer=self.regularizer, bias_regularizer=self.regularizer, trainable=trainable)
            self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus, default_gpu_id=default_gpu_id, random_seed=self.random_seed)

    def __call__(self, input_data, input_mask):
        """call convolutional highway layer"""
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
            transform, _ = self.dropout_layer(self.transform_layer(input_data), input_mask)
            gate = self.gate_layer(input_data)
            output_highway = transform * gate + input_data * (1 - gate)
            output_mask = input_mask
        return (output_highway, output_mask)


score

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

Class description:
Implement the NonNegativeOmpForestRegressor class.

Method signatures and docstrings:
- def predict(self, X, forest_size=None): Make prediction. If forest_size is None return the list of predictions of all intermediate solutions :param X: :return:
- def predict_no_weights(self, X, forest_size=None): Make a prediction of the selected trees but without weight. If forest_size is None return the list of unweighted predictions of all intermediate solutions. :param X: some data to apply the forest to :return: a np.array of the predictions of the trees selected by OMP without applying the weight
- def score(self, X, y, forest_size=None): Evaluate OMPForestClassifer on (`X`, `y`). if Idx_prediction is None return the score of all sub forest.` :param X: :param y: :return:

Implement the Python class `NonNegativeOmpForestRegressor` described below.

Class description:
Implement the NonNegativeOmpForestRegressor class.

Method signatures and docstrings:
- def predict(self, X, forest_size=None): Make prediction. If forest_size is None return the list of predictions of all intermediate solutions :param X: :return:
- def predict_no_weights(self, X, forest_size=None): Make a prediction of the selected trees but without weight. If forest_size is None return the list of unweighted predictions of all intermediate solutions. :param X: some data to apply the forest to :return: a np.array of the predictions of the trees selected by OMP without applying the weight
- def score(self, X, y, forest_size=None): Evaluate OMPForestClassifer on (`X`, `y`). if Idx_prediction is None return the score of all sub forest.` :param X: :param y: :return:

<|skeleton|>
class NonNegativeOmpForestRegressor:

    def predict(self, X, forest_size=None):
        """Make prediction. If forest_size is None return the list of predictions of all intermediate solutions :param X: :return:"""
        <|body_0|>

    def predict_no_weights(self, X, forest_size=None):
        """Make a prediction of the selected trees but without weight. If forest_size is None return the list of unweighted predictions of all intermediate solutions. :param X: some data to apply the forest to :return: a np.array of the predictions of the trees selected by OMP without applying the weight"""
        <|body_1|>

    def score(self, X, y, forest_size=None):
        """Evaluate OMPForestClassifer on (`X`, `y`). if Idx_prediction is None return the score of all sub forest.` :param X: :param y: :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        forest_predictions = self._base_estimator_predictions(X)
        if self._models_parameters.normalize_D:
            forest_predictions /= self._forest_norms
        return self._omp.predict(forest_predictions, forest_size)
<|end_body_0|>

<|body_start_1|>
        forest_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_])
        if forest_size is not None:
            weights = self._omp.get_coef(forest_size)
            select_trees = np.mean(forest_predictions[weights != 0], axis=0)
            return select_trees
        else:
            lst_predictions = []
            for sol in self._omp.get_coef():
                lst_predictions.append(np.mean(forest_predictions[sol != 0], axis=0))
            return lst_predictions
<|end_body_1|>

<|body_start_2|>
        if forest_size is not None:
            predictions = self.predict(X, forest_size)
            return np.mean(np.square(predictions - y))
        else:
            predictions = self.predict(X)
            lst_scores = []
            for pred in predictions:
                lst_scores.append(np.mean(np.square(pred - y)))
            return lst_scores
<|end_body_2|>

Solution:
class NonNegativeOmpForestRegressor:
    def predict(self, X, forest_size=None):
        """Make prediction. If forest_size is None return the list of predictions of all intermediate solutions :param X: :return:"""
        forest_predictions = self._base_estimator_predictions(X)
        if self._models_parameters.normalize_D:
            forest_predictions /= self._forest_norms
        return self._omp.predict(forest_predictions, forest_size)

    def predict_no_weights(self, X, forest_size=None):
        """Make a prediction of the selected trees but without weight. If forest_size is None return the list of unweighted predictions of all intermediate solutions. :param X: some data to apply the forest to :return: a np.array of the predictions of the trees selected by OMP without applying the weight"""
        forest_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_])
        if forest_size is not None:
            weights = self._omp.get_coef(forest_size)
            select_trees = np.mean(forest_predictions[weights != 0], axis=0)
            return select_trees
        else:
            lst_predictions = []
            for sol in self._omp.get_coef():
                lst_predictions.append(np.mean(forest_predictions[sol != 0], axis=0))
            return lst_predictions

    def score(self, X, y, forest_size=None):
        """Evaluate OMPForestClassifer on (`X`, `y`). if Idx_prediction is None return the score of all sub forest.` :param X: :param y: :return:"""
        if forest_size is not None:
            predictions = self.predict(X, forest_size)
            return np.mean(np.square(predictions - y))
        else:
            predictions = self.predict(X)
            lst_scores = []
            for pred in predictions:
                lst_scores.append(np.mean(np.square(pred - y)))
            return lst_scores


sample

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

Class description:
An audio transform parameter with min and max value.

Method signatures and docstrings:
- def __init__(self, name, min_value, max_value, scale): Initialize an AudioTransformParameter. Args: name: The name of the parameter. Should be the same as the name of the parameter passed to sox. min_value: The minimum value of the parameter, a float. max_value: The maximum value of the parameter, a float. scale: 'linear' or 'log', the scale with which to sample the parameter value. Raises: ValueError: If `scale` is not 'linear' or 'log'.
- def sample(self): Sample the parameter, returning a random value in its range. Returns: A value drawn uniformly at random between `min_value` and `max_value`.

Implement the Python class `AudioTransformParameter` described below.

Class description:
An audio transform parameter with min and max value.

Method signatures and docstrings:
- def __init__(self, name, min_value, max_value, scale): Initialize an AudioTransformParameter. Args: name: The name of the parameter. Should be the same as the name of the parameter passed to sox. min_value: The minimum value of the parameter, a float. max_value: The maximum value of the parameter, a float. scale: 'linear' or 'log', the scale with which to sample the parameter value. Raises: ValueError: If `scale` is not 'linear' or 'log'.
- def sample(self): Sample the parameter, returning a random value in its range. Returns: A value drawn uniformly at random between `min_value` and `max_value`.

<|skeleton|>
class AudioTransformParameter:
    """An audio transform parameter with min and max value."""

    def __init__(self, name, min_value, max_value, scale):
        """Initialize an AudioTransformParameter. Args: name: The name of the parameter. Should be the same as the name of the parameter passed to sox. min_value: The minimum value of the parameter, a float. max_value: The maximum value of the parameter, a float. scale: 'linear' or 'log', the scale with which to sample the parameter value. Raises: ValueError: If `scale` is not 'linear' or 'log'."""
        <|body_0|>

    def sample(self):
        """Sample the parameter, returning a random value in its range. Returns: A value drawn uniformly at random between `min_value` and `max_value`."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if scale not in ('linear', 'log'):
            raise ValueError('invalid parameter scale: %s' % scale)
        self.name = name
        self.min_value = min_value
        self.max_value = max_value
        self.scale = scale
<|end_body_0|>

<|body_start_1|>
        if self.scale == 'linear':
            return random.uniform(self.min_value, self.max_value)
        else:
            log_min_value = math.log(self.min_value)
            log_max_value = math.log(self.max_value)
            return math.exp(random.uniform(log_min_value, log_max_value))
<|end_body_1|>

Solution:
class AudioTransformParameter:
    """An audio transform parameter with min and max value."""

    def __init__(self, name, min_value, max_value, scale):
        """Initialize an AudioTransformParameter. Args: name: The name of the parameter. Should be the same as the name of the parameter passed to sox. min_value: The minimum value of the parameter, a float. max_value: The maximum value of the parameter, a float. scale: 'linear' or 'log', the scale with which to sample the parameter value. Raises: ValueError: If `scale` is not 'linear' or 'log'."""
        if scale not in ('linear', 'log'):
            raise ValueError('invalid parameter scale: %s' % scale)
        self.name = name
        self.min_value = min_value
        self.max_value = max_value
        self.scale = scale

    def sample(self):
        """Sample the parameter, returning a random value in its range. Returns: A value drawn uniformly at random between `min_value` and `max_value`."""
        if self.scale == 'linear':
            return random.uniform(self.min_value, self.max_value)
        else:
            log_min_value = math.log(self.min_value)
            log_max_value = math.log(self.max_value)
            return math.exp(random.uniform(log_min_value, log_max_value))


knightProbability

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def knightProbability(self, N: int, K: int, r: int, c: int) -> float: brute force K step with memory, it is considered dp
- def knightProbability_error(self, N: int, K: int, r: int, c: int) -> float: brute force K step

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def knightProbability(self, N: int, K: int, r: int, c: int) -> float: brute force K step with memory, it is considered dp
- def knightProbability_error(self, N: int, K: int, r: int, c: int) -> float: brute force K step

<|skeleton|>
class Solution:

    def knightProbability(self, N: int, K: int, r: int, c: int) -> float:
        """brute force K step with memory, it is considered dp"""
        <|body_0|>

    def knightProbability_error(self, N: int, K: int, r: int, c: int) -> float:
        """brute force K step"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        q = set([(r, c)])
        P = [[0 for _ in range(N)] for _ in range(N)]
        P[r][c] = 1
        k = 0
        while k < K:
            k += 1
            cur_q = set()
            cur_P = [[0 for _ in range(N)] for _ in range(N)]
            for i, j in q:
                for di, dj in dirs:
                    I = i + di
                    J = j + dj
                    if 0 <= I < N and 0 <= J < N:
                        cur_q.add((I, J))
                        cur_P[I][J] += P[i][j] * 1 / 8
            q = cur_q
            P = cur_P
        return sum([P[i][j] for i in range(N) for j in range(N)])
<|end_body_0|>

<|body_start_1|>
        q = [(r, c)]
        P = [[0 for _ in range(N)] for _ in range(N)]
        P[r][c] = 1
        k = 0
        while k < K:
            k += 1
            cur_q = []
            cur_P = [[0 for _ in range(N)] for _ in range(N)]
            for i, j in q:
                for di, dj in dirs:
                    I = i + di
                    J = j + dj
                    if 0 <= I < N and 0 <= J < N:
                        cur_q.append((I, J))
                        cur_P[I][J] += P[i][j] * 1 / 8
            q = cur_q
            P = cur_P
        return sum([P[i][j] for i in range(N) for j in range(N)])
<|end_body_1|>

Solution:
class Solution:
    def knightProbability(self, N: int, K: int, r: int, c: int) -> float:
        """brute force K step with memory, it is considered dp"""
        q = set([(r, c)])
        P = [[0 for _ in range(N)] for _ in range(N)]
        P[r][c] = 1
        k = 0
        while k < K:
            k += 1
            cur_q = set()
            cur_P = [[0 for _ in range(N)] for _ in range(N)]
            for i, j in q:
                for di, dj in dirs:
                    I = i + di
                    J = j + dj
                    if 0 <= I < N and 0 <= J < N:
                        cur_q.add((I, J))
                        cur_P[I][J] += P[i][j] * 1 / 8
            q = cur_q
            P = cur_P
        return sum([P[i][j] for i in range(N) for j in range(N)])

    def knightProbability_error(self, N: int, K: int, r: int, c: int) -> float:
        """brute force K step"""
        q = [(r, c)]
        P = [[0 for _ in range(N)] for _ in range(N)]
        P[r][c] = 1
        k = 0
        while k < K:
            k += 1
            cur_q = []
            cur_P = [[0 for _ in range(N)] for _ in range(N)]
            for i, j in q:
                for di, dj in dirs:
                    I = i + di
                    J = j + dj
                    if 0 <= I < N and 0 <= J < N:
                        cur_q.append((I, J))
                        cur_P[I][J] += P[i][j] * 1 / 8
            q = cur_q
            P = cur_P
        return sum([P[i][j] for i in range(N) for j in range(N)])

_algorithms_challenges/leetcode/LeetCode/688 Knight Probability in Chessboard.py


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

Class description:
Tests for the BuildOrder LIST API.

Method signatures and docstrings:
- def test_get_all_builds(self): Retrieve *all* builds via the API.
- def test_overdue(self): Create a new build, in the past.
- def test_sub_builds(self): Test the build / sub-build relationship.

Implement the Python class `BuildListTest` described below.

Class description:
Tests for the BuildOrder LIST API.

Method signatures and docstrings:
- def test_get_all_builds(self): Retrieve *all* builds via the API.
- def test_overdue(self): Create a new build, in the past.
- def test_sub_builds(self): Test the build / sub-build relationship.

<|skeleton|>
class BuildListTest:
    """Tests for the BuildOrder LIST API."""

    def test_get_all_builds(self):
        """Retrieve *all* builds via the API."""
        <|body_0|>

    def test_overdue(self):
        """Create a new build, in the past."""
        <|body_1|>

    def test_sub_builds(self):
        """Test the build / sub-build relationship."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        builds = self.get(self.url)
        self.assertEqual(len(builds.data), 5)
        builds = self.get(self.url, data={'active': True})
        self.assertEqual(len(builds.data), 1)
        builds = self.get(self.url, data={'status': BuildStatus.COMPLETE.value})
        self.assertEqual(len(builds.data), 4)
        builds = self.get(self.url, data={'overdue': False})
        self.assertEqual(len(builds.data), 5)
        builds = self.get(self.url, data={'overdue': True})
        self.assertEqual(len(builds.data), 0)
<|end_body_0|>

<|body_start_1|>
        in_the_past = datetime.now().date() - timedelta(days=50)
        part = Part.objects.get(pk=50)
        Build.objects.create(part=part, reference='BO-0006', quantity=10, title='Just some thing', status=BuildStatus.PRODUCTION.value, target_date=in_the_past)
        response = self.get(self.url, data={'overdue': True})
        builds = response.data
        self.assertEqual(len(builds), 1)
<|end_body_1|>

<|body_start_2|>
        parent = Build.objects.get(pk=5)
        part = Part.objects.get(pk=50)
        n = Build.objects.count()
        for i in range(5):
            Build.objects.create(part=part, quantity=10, reference=f'BO-{i + 10}', title=f'Sub build {i}', parent=parent)
        for ii, sub_build in enumerate(Build.objects.filter(parent=parent)):
            for i in range(3):
                x = ii * 10 + i + 50
                Build.objects.create(part=part, reference=f'BO-{x}', title=f'{sub_build.reference}-00{i}-sub', quantity=40, parent=sub_build)
        self.assertEqual(Build.objects.count(), n + 20)
        Build.objects.rebuild()
        response = self.get(self.url, data={'parent': parent.pk})
        builds = response.data
        self.assertEqual(len(builds), 5)
        response = self.get(self.url, data={'ancestor': parent.pk})
        builds = response.data
        self.assertEqual(len(builds), 20)
<|end_body_2|>

Solution:
class BuildListTest:
    """Tests for the BuildOrder LIST API."""

    def test_get_all_builds(self):
        """Retrieve *all* builds via the API."""
        builds = self.get(self.url)
        self.assertEqual(len(builds.data), 5)
        builds = self.get(self.url, data={'active': True})
        self.assertEqual(len(builds.data), 1)
        builds = self.get(self.url, data={'status': BuildStatus.COMPLETE.value})
        self.assertEqual(len(builds.data), 4)
        builds = self.get(self.url, data={'overdue': False})
        self.assertEqual(len(builds.data), 5)
        builds = self.get(self.url, data={'overdue': True})
        self.assertEqual(len(builds.data), 0)

    def test_overdue(self):
        """Create a new build, in the past."""
        in_the_past = datetime.now().date() - timedelta(days=50)
        part = Part.objects.get(pk=50)
        Build.objects.create(part=part, reference='BO-0006', quantity=10, title='Just some thing', status=BuildStatus.PRODUCTION.value, target_date=in_the_past)
        response = self.get(self.url, data={'overdue': True})
        builds = response.data
        self.assertEqual(len(builds), 1)

    def test_sub_builds(self):
        """Test the build / sub-build relationship."""
        parent = Build.objects.get(pk=5)
        part = Part.objects.get(pk=50)
        n = Build.objects.count()
        for i in range(5):
            Build.objects.create(part=part, quantity=10, reference=f'BO-{i + 10}', title=f'Sub build {i}', parent=parent)
        for ii, sub_build in enumerate(Build.objects.filter(parent=parent)):
            for i in range(3):
                x = ii * 10 + i + 50
                Build.objects.create(part=part, reference=f'BO-{x}', title=f'{sub_build.reference}-00{i}-sub', quantity=40, parent=sub_build)
        self.assertEqual(Build.objects.count(), n + 20)
        Build.objects.rebuild()
        response = self.get(self.url, data={'parent': parent.pk})
        builds = response.data
        self.assertEqual(len(builds), 5)
        response = self.get(self.url, data={'ancestor': parent.pk})
        builds = response.data
        self.assertEqual(len(builds), 20)


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

Class description:
Implement the ORM_Rule class.

Method signatures and docstrings:
- def __init__(self, base_purities, idx_var, inter, rule, rule_type): Initialize the caracteristics of a rule. Parameters: - basePurities, dict. The purity of the training vector regarding the target. k: class modality and v: the purity for the corresponding class modality. - idx_var, int or list. index of the variable(s) - inter, list or list of list. intervale(s) (i.e., the condition(s) on the feature(s)) - rule, list of tuple. The tuple are under the form (tgt, (vf1, vf2)) where tgt correspond to the target value within the rule and vf1, vf2 correspond to the sample's values (int) for the features within the rule. - rule_type, str or list. The rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the bo
- def is_relevant_rule(self, mod_size_threshold, size_threshold, purity_threshold, z_score_threshold): Test if a rule is relevant according to rule quality measures threshold. Parameters: - mod_size_threshold, dict of float. The modality size threshold for each one of the modalities. - size_threshold, dict of float. The size threshold for each one of the modalities. - purity_threshold, dict of float. The purity threshold for each one of the modalities. - z_score_threshold, float. The z-score threshold for each one of the modalities. Output: return True if the rule is relevant (i.e., the rule quality measures of the rule pass the threshold), False either.
- def get_rule_metadata(self): Save the rules caracteristics. Output: return a np.array of shape = [1,8] containing the rules caracteristics in the fellowing order: 1) index of the variable(s), 2) intervals (i.e., the condition(s) on the feature(s)), 3) the size of the rule (i.e., the number of suject in the rule), 4) most representing modality, 5) the rule modality size (i.e., the number of subject regarding the majority class of the rule), 6) the rule purity, 7) the rule z-score and 8) the rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the both ('c','d')).

Implement the Python class `ORM_Rule` described below.

Class description:
Implement the ORM_Rule class.

Method signatures and docstrings:
- def __init__(self, base_purities, idx_var, inter, rule, rule_type): Initialize the caracteristics of a rule. Parameters: - basePurities, dict. The purity of the training vector regarding the target. k: class modality and v: the purity for the corresponding class modality. - idx_var, int or list. index of the variable(s) - inter, list or list of list. intervale(s) (i.e., the condition(s) on the feature(s)) - rule, list of tuple. The tuple are under the form (tgt, (vf1, vf2)) where tgt correspond to the target value within the rule and vf1, vf2 correspond to the sample's values (int) for the features within the rule. - rule_type, str or list. The rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the bo
- def is_relevant_rule(self, mod_size_threshold, size_threshold, purity_threshold, z_score_threshold): Test if a rule is relevant according to rule quality measures threshold. Parameters: - mod_size_threshold, dict of float. The modality size threshold for each one of the modalities. - size_threshold, dict of float. The size threshold for each one of the modalities. - purity_threshold, dict of float. The purity threshold for each one of the modalities. - z_score_threshold, float. The z-score threshold for each one of the modalities. Output: return True if the rule is relevant (i.e., the rule quality measures of the rule pass the threshold), False either.
- def get_rule_metadata(self): Save the rules caracteristics. Output: return a np.array of shape = [1,8] containing the rules caracteristics in the fellowing order: 1) index of the variable(s), 2) intervals (i.e., the condition(s) on the feature(s)), 3) the size of the rule (i.e., the number of suject in the rule), 4) most representing modality, 5) the rule modality size (i.e., the number of subject regarding the majority class of the rule), 6) the rule purity, 7) the rule z-score and 8) the rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the both ('c','d')).

<|skeleton|>
class ORM_Rule:

    def __init__(self, base_purities, idx_var, inter, rule, rule_type):
        """Initialize the caracteristics of a rule. Parameters: - basePurities, dict. The purity of the training vector regarding the target. k: class modality and v: the purity for the corresponding class modality. - idx_var, int or list. index of the variable(s) - inter, list or list of list. intervale(s) (i.e., the condition(s) on the feature(s)) - rule, list of tuple. The tuple are under the form (tgt, (vf1, vf2)) where tgt correspond to the target value within the rule and vf1, vf2 correspond to the sample's values (int) for the features within the rule. - rule_type, str or list. The rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the bo"""
        <|body_0|>

    def is_relevant_rule(self, mod_size_threshold, size_threshold, purity_threshold, z_score_threshold):
        """Test if a rule is relevant according to rule quality measures threshold. Parameters: - mod_size_threshold, dict of float. The modality size threshold for each one of the modalities. - size_threshold, dict of float. The size threshold for each one of the modalities. - purity_threshold, dict of float. The purity threshold for each one of the modalities. - z_score_threshold, float. The z-score threshold for each one of the modalities. Output: return True if the rule is relevant (i.e., the rule quality measures of the rule pass the threshold), False either."""
        <|body_1|>

    def get_rule_metadata(self):
        """Save the rules caracteristics. Output: return a np.array of shape = [1,8] containing the rules caracteristics in the fellowing order: 1) index of the variable(s), 2) intervals (i.e., the condition(s) on the feature(s)), 3) the size of the rule (i.e., the number of suject in the rule), 4) most representing modality, 5) the rule modality size (i.e., the number of subject regarding the majority class of the rule), 6) the rule purity, 7) the rule z-score and 8) the rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the both ('c','d'))."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.idx_var = idx_var
        self.inter = inter
        self.base_purities = base_purities
        self.rule = rule
        self.rule_type = rule_type
        self.rule_mod_sizes = Counter((i[0] for i in self.rule))
        self.rule_size = float(sum(self.rule_mod_sizes.values()))
        self.rule_purities = {k: v / self.rule_size for k, v in self.rule_mod_sizes.iteritems()}

        def z_score(rule_size, rule_purity, base_purity):
            """
                    Compute the z-score of a rule regarding a particular modality.

                    Parameters:
                    - rule_size, int. (i.e., the number of suject in the rule)
                    - rule_purity, float. Rule purity.
                    - base_purity, float. Base purity.

                    return z-score
                    """
            if np.sqrt(base_purity * (1 - base_purity)) == 0:
                return 0
            else:
                return np.sqrt(rule_size) * (rule_purity - base_purity) / np.sqrt(base_purity * (1 - base_purity))
        self.rule_z_scores = {k: z_score(self.rule_size, self.rule_purities[k], self.base_purities[k]) for k, v in self.rule_purities.iteritems()}
        self.rule_interest_mod = key_with_max_val(self.rule_z_scores)
<|end_body_0|>

<|body_start_1|>
        rule_mod = str(int(self.rule_interest_mod))
        if self.rule_mod_sizes[self.rule_interest_mod] >= mod_size_threshold[rule_mod] and self.rule_size >= size_threshold[rule_mod] and (self.rule_purities[self.rule_interest_mod] >= purity_threshold[rule_mod]) and (self.rule_z_scores[self.rule_interest_mod] >= z_score_threshold[rule_mod]):
            return True
        else:
            return False
<|end_body_1|>

<|body_start_2|>
        if isinstance(self.idx_var, int):
            idx_var_ = [self.idx_var]
            inter_ = [self.inter]
            rule_type_ = [self.rule_type]
        else:
            idx_var_ = list(self.idx_var)
            inter_ = self.inter
            rule_type_ = self.rule_type
        return np.array([idx_var_, inter_, self.rule_size, self.rule_interest_mod, self.rule_mod_sizes[self.rule_interest_mod], self.rule_purities[self.rule_interest_mod], self.rule_z_scores[self.rule_interest_mod], rule_type_])
<|end_body_2|>

Solution:
class ORM_Rule:
    def __init__(self, base_purities, idx_var, inter, rule, rule_type):
        """Initialize the caracteristics of a rule. Parameters: - basePurities, dict. The purity of the training vector regarding the target. k: class modality and v: the purity for the corresponding class modality. - idx_var, int or list. index of the variable(s) - inter, list or list of list. intervale(s) (i.e., the condition(s) on the feature(s)) - rule, list of tuple. The tuple are under the form (tgt, (vf1, vf2)) where tgt correspond to the target value within the rule and vf1, vf2 correspond to the sample's values (int) for the features within the rule. - rule_type, str or list. The rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the bo"""
        self.idx_var = idx_var
        self.inter = inter
        self.base_purities = base_purities
        self.rule = rule
        self.rule_type = rule_type
        self.rule_mod_sizes = Counter((i[0] for i in self.rule))
        self.rule_size = float(sum(self.rule_mod_sizes.values()))
        self.rule_purities = {k: v / self.rule_size for k, v in self.rule_mod_sizes.iteritems()}

        def z_score(rule_size, rule_purity, base_purity):
            """
                    Compute the z-score of a rule regarding a particular modality.

                    Parameters:
                    - rule_size, int. (i.e., the number of suject in the rule)
                    - rule_purity, float. Rule purity.
                    - base_purity, float. Base purity.

                    return z-score
                    """
            if np.sqrt(base_purity * (1 - base_purity)) == 0:
                return 0
            else:
                return np.sqrt(rule_size) * (rule_purity - base_purity) / np.sqrt(base_purity * (1 - base_purity))
        self.rule_z_scores = {k: z_score(self.rule_size, self.rule_purities[k], self.base_purities[k]) for k, v in self.rule_purities.iteritems()}
        self.rule_interest_mod = key_with_max_val(self.rule_z_scores)

    def is_relevant_rule(self, mod_size_threshold, size_threshold, purity_threshold, z_score_threshold):
        """Test if a rule is relevant according to rule quality measures threshold. Parameters: - mod_size_threshold, dict of float. The modality size threshold for each one of the modalities. - size_threshold, dict of float. The size threshold for each one of the modalities. - purity_threshold, dict of float. The purity threshold for each one of the modalities. - z_score_threshold, float. The z-score threshold for each one of the modalities. Output: return True if the rule is relevant (i.e., the rule quality measures of the rule pass the threshold), False either."""
        rule_mod = str(int(self.rule_interest_mod))
        if self.rule_mod_sizes[self.rule_interest_mod] >= mod_size_threshold[rule_mod] and self.rule_size >= size_threshold[rule_mod] and (self.rule_purities[self.rule_interest_mod] >= purity_threshold[rule_mod]) and (self.rule_z_scores[self.rule_interest_mod] >= z_score_threshold[rule_mod]):
            return True
        else:
            return False

    def get_rule_metadata(self):
        """Save the rules caracteristics. Output: return a np.array of shape = [1,8] containing the rules caracteristics in the fellowing order: 1) index of the variable(s), 2) intervals (i.e., the condition(s) on the feature(s)), 3) the size of the rule (i.e., the number of suject in the rule), 4) most representing modality, 5) the rule modality size (i.e., the number of subject regarding the majority class of the rule), 6) the rule purity, 7) the rule z-score and 8) the rule type (i.e., if the rule comes from interger ordered feature(s) ('c') or categorical feature(s) ('d') or the both ('c','d'))."""
        if isinstance(self.idx_var, int):
            idx_var_ = [self.idx_var]
            inter_ = [self.inter]
            rule_type_ = [self.rule_type]
        else:
            idx_var_ = list(self.idx_var)
            inter_ = self.inter
            rule_type_ = self.rule_type
        return np.array([idx_var_, inter_, self.rule_size, self.rule_interest_mod, self.rule_mod_sizes[self.rule_interest_mod], self.rule_purities[self.rule_interest_mod], self.rule_z_scores[self.rule_interest_mod], rule_type_])


result

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

Class description:
局面☆（＾～＾）ゲームデータをセーブしたり、ロードしたりするときの保存されてる現状だぜ☆（＾～＾）

Method signatures and docstrings:
- def __init__(self): 規定値☆（＾～＾）
- def pos(self): 局面を表示するぜ☆（＾～＾） >>> from look_and_model import Position >>> pos = Position() >>> print(pos.pos()) [Next 1 move(s) | Go o] +---+---+---+ | | | | マスを選んでください。例 `do 7` +---+---+---+ | | | | 7 8 9 +---+---+---+ 4 5 6 | | | | 1 2 3 +---+---+---+ Returns ------- str: 局面
- def result(result: 'GameResult', winner: 'Piece'): 着いていれば勝敗を表示するぜ☆（＾～＾） 負けが表示されるケースは無い☆（＾～＾） >>> from look_and_model import Position >>> print(Position.result(GameResult.WIN, Piece.NOUGHT)) win o Returns ------- str: 着いていれば、勝敗

Implement the Python class `Position` described below.

Class description:
局面☆（＾～＾）ゲームデータをセーブしたり、ロードしたりするときの保存されてる現状だぜ☆（＾～＾）

Method signatures and docstrings:
- def __init__(self): 規定値☆（＾～＾）
- def pos(self): 局面を表示するぜ☆（＾～＾） >>> from look_and_model import Position >>> pos = Position() >>> print(pos.pos()) [Next 1 move(s) | Go o] +---+---+---+ | | | | マスを選んでください。例 `do 7` +---+---+---+ | | | | 7 8 9 +---+---+---+ 4 5 6 | | | | 1 2 3 +---+---+---+ Returns ------- str: 局面
- def result(result: 'GameResult', winner: 'Piece'): 着いていれば勝敗を表示するぜ☆（＾～＾） 負けが表示されるケースは無い☆（＾～＾） >>> from look_and_model import Position >>> print(Position.result(GameResult.WIN, Piece.NOUGHT)) win o Returns ------- str: 着いていれば、勝敗

<|skeleton|>
class Position:
    """局面☆（＾～＾）ゲームデータをセーブしたり、ロードしたりするときの保存されてる現状だぜ☆（＾～＾）"""

    def __init__(self):
        """規定値☆（＾～＾）"""
        <|body_0|>

    def pos(self):
        """局面を表示するぜ☆（＾～＾） >>> from look_and_model import Position >>> pos = Position() >>> print(pos.pos()) [Next 1 move(s) | Go o] +---+---+---+ | | | | マスを選んでください。例 `do 7` +---+---+---+ | | | | 7 8 9 +---+---+---+ 4 5 6 | | | | 1 2 3 +---+---+---+ Returns ------- str: 局面"""
        <|body_1|>

    def result(result: 'GameResult', winner: 'Piece'):
        """着いていれば勝敗を表示するぜ☆（＾～＾） 負けが表示されるケースは無い☆（＾～＾） >>> from look_and_model import Position >>> print(Position.result(GameResult.WIN, Piece.NOUGHT)) win o Returns ------- str: 着いていれば、勝敗"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        """
                次に盤に置く駒☆（＾～＾）
                英語では 手番は your turn, 相手版は your opponent's turn なんで 手番という英語は無い☆（＾～＾）
                自分という意味の単語はプログラム用語と被りまくるんで、
                あまり被らない 味方(friend) を手番の意味で たまたま使ってるだけだぜ☆（＾～＾）
                """
        self.friend = Piece.NOUGHT
        '開始局面の盤の各マス☆（＾～＾） [0] は未使用☆（＾～＾）'
        self.starting_board = [None] * BOARD_LEN
        '盤の上に最初から駒が何個置いてあったかだぜ☆（＾～＾）'
        self.starting_pieces_num = 0
        '現状の盤の各マス☆（＾～＾） [0] は未使用☆（＾～＾）'
        self.board = [None] * BOARD_LEN
        '棋譜だぜ☆（＾～＾）駒を置いた番地を並べてけだぜ☆（＾～＾）'
        self.history = [0] * SQUARES_NUM
        '盤の上に駒が何個置いてあるかだぜ☆（＾～＾）'
        self.pieces_num = 0
<|end_body_0|>

<|body_start_1|>
        def cell(index: int):
            """
                    Returns
                    -------
                    str
                        マスの横幅に合わせた石、または空欄。
                    """
            if self.board[index] is None:
                return '   '
            else:
                return f' {self.board[index]} '
        s = f'[Next {self.pieces_num + 1} move(s) | Go {self.friend}]\n\n'
        s += '+---+---+---+\n|{0}|{1}|{2}| マスを選んでください。例 `do 7`\n+---+---+---+\n|{3}|{4}|{5}|    7 8 9\n+---+---+---+    4 5 6\n|{6}|{7}|{8}|    1 2 3\n+---+---+---+'.format(cell(7), cell(8), cell(9), cell(4), cell(5), cell(6), cell(1), cell(2), cell(3))
        return s
<|end_body_1|>

<|body_start_2|>
        if result == GameResult.WIN:
            return f'win {winner}'
        elif result == GameResult.DRAW:
            return f'draw'
        else:
            None
<|end_body_2|>

Solution:
class Position:
    """局面☆（＾～＾）ゲームデータをセーブしたり、ロードしたりするときの保存されてる現状だぜ☆（＾～＾）"""

    def __init__(self):
        """規定値☆（＾～＾）"""
        """
                次に盤に置く駒☆（＾～＾）
                英語では 手番は your turn, 相手版は your opponent's turn なんで 手番という英語は無い☆（＾～＾）
                自分という意味の単語はプログラム用語と被りまくるんで、
                あまり被らない 味方(friend) を手番の意味で たまたま使ってるだけだぜ☆（＾～＾）
                """
        self.friend = Piece.NOUGHT
        '開始局面の盤の各マス☆（＾～＾） [0] は未使用☆（＾～＾）'
        self.starting_board = [None] * BOARD_LEN
        '盤の上に最初から駒が何個置いてあったかだぜ☆（＾～＾）'
        self.starting_pieces_num = 0
        '現状の盤の各マス☆（＾～＾） [0] は未使用☆（＾～＾）'
        self.board = [None] * BOARD_LEN
        '棋譜だぜ☆（＾～＾）駒を置いた番地を並べてけだぜ☆（＾～＾）'
        self.history = [0] * SQUARES_NUM
        '盤の上に駒が何個置いてあるかだぜ☆（＾～＾）'
        self.pieces_num = 0

    def pos(self):
        """局面を表示するぜ☆（＾～＾） >>> from look_and_model import Position >>> pos = Position() >>> print(pos.pos()) [Next 1 move(s) | Go o] +---+---+---+ | | | | マスを選んでください。例 `do 7` +---+---+---+ | | | | 7 8 9 +---+---+---+ 4 5 6 | | | | 1 2 3 +---+---+---+ Returns ------- str: 局面"""
        def cell(index: int):
            """
                    Returns
                    -------
                    str
                        マスの横幅に合わせた石、または空欄。
                    """
            if self.board[index] is None:
                return '   '
            else:
                return f' {self.board[index]} '
        s = f'[Next {self.pieces_num + 1} move(s) | Go {self.friend}]\n\n'
        s += '+---+---+---+\n|{0}|{1}|{2}| マスを選んでください。例 `do 7`\n+---+---+---+\n|{3}|{4}|{5}|    7 8 9\n+---+---+---+    4 5 6\n|{6}|{7}|{8}|    1 2 3\n+---+---+---+'.format(cell(7), cell(8), cell(9), cell(4), cell(5), cell(6), cell(1), cell(2), cell(3))
        return s

    def result(result: 'GameResult', winner: 'Piece'):
        """着いていれば勝敗を表示するぜ☆（＾～＾） 負けが表示されるケースは無い☆（＾～＾） >>> from look_and_model import Position >>> print(Position.result(GameResult.WIN, Piece.NOUGHT)) win o Returns ------- str: 着いていれば、勝敗"""
        if result == GameResult.WIN:
            return f'win {winner}'
        elif result == GameResult.DRAW:
            return f'draw'
        else:
            None


forward

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

Class description:
Implement the DCN class.

Method signatures and docstrings:
- def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_layer=2): :param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: :param hidden_dims: :param num_classes: :param dropout:
- def forward(self, X_sparse, X_dense=None): X_sparse: sparse_feature [batch_size, sparse_feature_num] X_dense: dense_feature [batch_size, dense_feature_num]

Implement the Python class `DCN` described below.

Class description:
Implement the DCN class.

Method signatures and docstrings:
- def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_layer=2): :param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: :param hidden_dims: :param num_classes: :param dropout:
- def forward(self, X_sparse, X_dense=None): X_sparse: sparse_feature [batch_size, sparse_feature_num] X_dense: dense_feature [batch_size, dense_feature_num]

<|skeleton|>
class DCN:

    def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_layer=2):
        """:param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: :param hidden_dims: :param num_classes: :param dropout:"""
        <|body_0|>

    def forward(self, X_sparse, X_dense=None):
        """X_sparse: sparse_feature [batch_size, sparse_feature_num] X_dense: dense_feature [batch_size, dense_feature_num]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(DCN, self).__init__()
        self.cate_fea_size = len(cate_fea_uniques)
        self.num_fea_size = num_fea_size
        self.num_layers = num_layers
        self.sparse_embedding = nn.ModuleList([nn.Embedding(voc_size, emb_size) for voc_size in cate_fea_uniques])
        self.cross_layer = Cross_Layer()
        self.all_dims = [self.cate_fea_size * emb_size + self.num_fea_size] + hidden_dims
        for i in range(1, len(self.all_dims)):
            setattr(self, 'linear_' + str(i), nn.Linear(self.all_dims[i - 1], self.all_dims[i]))
            setattr(self, 'batchNorm_' + str(i), nn.BatchNorm1d(self.all_dims[i]))
            setattr(self, 'activation_' + str(i), nn.ReLU())
            setattr(self, 'dropout_' + str(i), nn.Dropout(dropout[i - 1]))
        self.output = nn.Linear(hidden_dims[-1] + self.cate_fea_size * emb_size + self.num_fea_size, 1)
        self.sigmoid = nn.Sigmoid()
<|end_body_0|>

<|body_start_1|>
        batch_size = X_sparse.size(0)
        sparse_feature_embed = [emb(X_sparse[:, i].unsqueeze(1)) for i, emb in enumerate(self.sparse_embedding)]
        sparse_feature_embed = torch.cat(sparse_feature_embed, dim=1)
        concat_sparse_inputs = sparse_feature_embed.view(batch_size, -1)
        embed_input = torch.cat((concat_sparse_inputs, X_dense), dim=-1)
        x1 = x0 = embed_input
        for i in range(self.num_layers):
            x1 = self.cross_layer(x0, x1)
        cross_layer_output = x1
        dnn_input = embed_input
        for i in range(1, len(self.all_dims)):
            dnn_input = getattr(self, 'linear_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'batchNorm_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'activation_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'dropout_' + str(i))(dnn_input)
        stack_output = torch.cat((cross_layer_output, dnn_input), dim=-1)
        out = self.output(stack_output)
        out = self.sigmoid(out)
        return out
<|end_body_1|>

Solution:
class DCN:
    def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_layer=2):
        """:param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: :param hidden_dims: :param num_classes: :param dropout:"""
        super(DCN, self).__init__()
        self.cate_fea_size = len(cate_fea_uniques)
        self.num_fea_size = num_fea_size
        self.num_layers = num_layers
        self.sparse_embedding = nn.ModuleList([nn.Embedding(voc_size, emb_size) for voc_size in cate_fea_uniques])
        self.cross_layer = Cross_Layer()
        self.all_dims = [self.cate_fea_size * emb_size + self.num_fea_size] + hidden_dims
        for i in range(1, len(self.all_dims)):
            setattr(self, 'linear_' + str(i), nn.Linear(self.all_dims[i - 1], self.all_dims[i]))
            setattr(self, 'batchNorm_' + str(i), nn.BatchNorm1d(self.all_dims[i]))
            setattr(self, 'activation_' + str(i), nn.ReLU())
            setattr(self, 'dropout_' + str(i), nn.Dropout(dropout[i - 1]))
        self.output = nn.Linear(hidden_dims[-1] + self.cate_fea_size * emb_size + self.num_fea_size, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, X_sparse, X_dense=None):
        """X_sparse: sparse_feature [batch_size, sparse_feature_num] X_dense: dense_feature [batch_size, dense_feature_num]"""
        batch_size = X_sparse.size(0)
        sparse_feature_embed = [emb(X_sparse[:, i].unsqueeze(1)) for i, emb in enumerate(self.sparse_embedding)]
        sparse_feature_embed = torch.cat(sparse_feature_embed, dim=1)
        concat_sparse_inputs = sparse_feature_embed.view(batch_size, -1)
        embed_input = torch.cat((concat_sparse_inputs, X_dense), dim=-1)
        x1 = x0 = embed_input
        for i in range(self.num_layers):
            x1 = self.cross_layer(x0, x1)
        cross_layer_output = x1
        dnn_input = embed_input
        for i in range(1, len(self.all_dims)):
            dnn_input = getattr(self, 'linear_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'batchNorm_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'activation_' + str(i))(dnn_input)
            dnn_input = getattr(self, 'dropout_' + str(i))(dnn_input)
        stack_output = torch.cat((cross_layer_output, dnn_input), dim=-1)
        out = self.output(stack_output)
        out = self.sigmoid(out)
        return out


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numDecodings(self, s): :type s: str :rtype: int
- def numDecodings2(self, s): :type s: str :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numDecodings(self, s): :type s: str :rtype: int
- def numDecodings2(self, s): :type s: str :rtype: int

<|skeleton|>
class Solution:

    def numDecodings(self, s):
        """:type s: str :rtype: int"""
        <|body_0|>

    def numDecodings2(self, s):
        """:type s: str :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.res = 0

        def solve(tmps):
            if len(tmps) == 0:
                self.res += 1
                return
            for i in range(min(len(tmps), 2)):
                if int(tmps[:i + 1]) > 0 and int(tmps[:i + 1]) <= 26:
                    solve(tmps[i + 1:])
                else:
                    return
        solve(s)
        return self.res
<|end_body_0|>

<|body_start_1|>
        n = len(s)
        if n == 0:
            return 0
        dp = [0 for _ in range(n + 1)]
        dp[n] = 1
        if s[n - 1] == '0':
            dp[n - 1] = 0
        else:
            dp[n - 1] = 1
        for i in range(n - 2, -1, -1):
            if int(s[i]) == 0:
                dp[i] = 0
            elif int(s[i:i + 2]) <= 26:
                dp[i] = dp[i + 1] + dp[i + 2]
            else:
                dp[i] = dp[i + 1]
        return dp[0]
<|end_body_1|>

Solution:
class Solution:
    def numDecodings(self, s):
        """:type s: str :rtype: int"""
        self.res = 0

        def solve(tmps):
            if len(tmps) == 0:
                self.res += 1
                return
            for i in range(min(len(tmps), 2)):
                if int(tmps[:i + 1]) > 0 and int(tmps[:i + 1]) <= 26:
                    solve(tmps[i + 1:])
                else:
                    return
        solve(s)
        return self.res

    def numDecodings2(self, s):
        """:type s: str :rtype: int"""
        n = len(s)
        if n == 0:
            return 0
        dp = [0 for _ in range(n + 1)]
        dp[n] = 1
        if s[n - 1] == '0':
            dp[n - 1] = 0
        else:
            dp[n - 1] = 1
        for i in range(n - 2, -1, -1):
            if int(s[i]) == 0:
                dp[i] = 0
            elif int(s[i:i + 2]) <= 26:
                dp[i] = dp[i + 1] + dp[i + 2]
            else:
                dp[i] = dp[i + 1]
        return dp[0]


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

Class description:
**Loads and provides access to GHG incentives.**

Method signatures and docstrings:
- def get_production_multiplier(vehicle): Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0
- def init_from_file(filename, verbose=False): Initialize class data from input file. Args: filename (str): name of input file verbose (bool): enable additional console and logfile output if True Returns: List of template/input errors, else empty list on success

Implement the Python class `Incentives` described below.

Class description:
**Loads and provides access to GHG incentives.**

Method signatures and docstrings:
- def get_production_multiplier(vehicle): Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0
- def init_from_file(filename, verbose=False): Initialize class data from input file. Args: filename (str): name of input file verbose (bool): enable additional console and logfile output if True Returns: List of template/input errors, else empty list on success

<|skeleton|>
class Incentives:
    """**Loads and provides access to GHG incentives.**"""

    def get_production_multiplier(vehicle):
        """Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0"""
        <|body_0|>

    def init_from_file(filename, verbose=False):
        """Initialize class data from input file. Args: filename (str): name of input file verbose (bool): enable additional console and logfile output if True Returns: List of template/input errors, else empty list on success"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        production_multiplier = 1
        start_years = Incentives._data['start_year']
        if len(start_years[start_years <= vehicle.model_year]) > 0:
            cache_key = max(start_years[start_years <= vehicle.model_year])
            if cache_key in Incentives._data:
                calcs = Incentives._data[cache_key]
                for calc, multiplier in calcs.items():
                    select_attribute, select_value = calc.split(':')
                    if vehicle.__getattribute__(select_attribute) == select_value:
                        production_multiplier = multiplier
        return production_multiplier
<|end_body_0|>

<|body_start_1|>
        Incentives._data.clear()
        if verbose:
            omega_log.logwrite('\nInitializing database from %s...' % filename)
        input_template_name = 'production_multipliers'
        input_template_version = 0.21
        input_template_columns = {'start_year'}
        template_errors = validate_template_version_info(filename, input_template_name, input_template_version, verbose=verbose)
        if not template_errors:
            df = pd.read_csv(filename, skiprows=1)
            template_errors = validate_template_column_names(filename, input_template_columns, df.columns, verbose=verbose)
            if not template_errors:
                df = df.set_index('start_year')
                df = df.drop([c for c in df.columns if 'Unnamed' in c], axis='columns')
                Incentives._data = df.to_dict(orient='index')
                Incentives._data['start_year'] = np.array([*Incentives._data])
        return template_errors
<|end_body_1|>

Solution:
class Incentives:
    """**Loads and provides access to GHG incentives.**"""

    def get_production_multiplier(vehicle):
        """Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0"""
        production_multiplier = 1
        start_years = Incentives._data['start_year']
        if len(start_years[start_years <= vehicle.model_year]) > 0:
            cache_key = max(start_years[start_years <= vehicle.model_year])
            if cache_key in Incentives._data:
                calcs = Incentives._data[cache_key]
                for calc, multiplier in calcs.items():
                    select_attribute, select_value = calc.split(':')
                    if vehicle.__getattribute__(select_attribute) == select_value:
                        production_multiplier = multiplier
        return production_multiplier

    def init_from_file(filename, verbose=False):
        """Initialize class data from input file. Args: filename (str): name of input file verbose (bool): enable additional console and logfile output if True Returns: List of template/input errors, else empty list on success"""
        Incentives._data.clear()
        if verbose:
            omega_log.logwrite('\nInitializing database from %s...' % filename)
        input_template_name = 'production_multipliers'
        input_template_version = 0.21
        input_template_columns = {'start_year'}
        template_errors = validate_template_version_info(filename, input_template_name, input_template_version, verbose=verbose)
        if not template_errors:
            df = pd.read_csv(filename, skiprows=1)
            template_errors = validate_template_column_names(filename, input_template_columns, df.columns, verbose=verbose)
            if not template_errors:
                df = df.set_index('start_year')
                df = df.drop([c for c in df.columns if 'Unnamed' in c], axis='columns')
                Incentives._data = df.to_dict(orient='index')
                Incentives._data['start_year'] = np.array([*Incentives._data])
        return template_errors


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

Class description:
Implement the negative_pi class.

Method signatures and docstrings:
- def __init__(self, level_fn=None, temperature=None, **kwargs): Arguments level_fn : function returning the level w.r.t. improvement is measured temperature : temperature parameter for soft_less
- def _closed_form(self, means, var, levels=None, level_fn=None, **kwargs): Closed-form expression for negative, marginal PI.
- def _monte_carlo(self, means, cov, levels=None, samples=None, weights=None, num_fantasies=None, temperature=None, incumbents=None, level_fn=None, **kwargs): Monte Carlo estimate of negative q-EI.

Implement the Python class `negative_pi` described below.

Class description:
Implement the negative_pi class.

Method signatures and docstrings:
- def __init__(self, level_fn=None, temperature=None, **kwargs): Arguments level_fn : function returning the level w.r.t. improvement is measured temperature : temperature parameter for soft_less
- def _closed_form(self, means, var, levels=None, level_fn=None, **kwargs): Closed-form expression for negative, marginal PI.
- def _monte_carlo(self, means, cov, levels=None, samples=None, weights=None, num_fantasies=None, temperature=None, incumbents=None, level_fn=None, **kwargs): Monte Carlo estimate of negative q-EI.

<|skeleton|>
class negative_pi:

    def __init__(self, level_fn=None, temperature=None, **kwargs):
        """Arguments level_fn : function returning the level w.r.t. improvement is measured temperature : temperature parameter for soft_less"""
        <|body_0|>

    def _closed_form(self, means, var, levels=None, level_fn=None, **kwargs):
        """Closed-form expression for negative, marginal PI."""
        <|body_1|>

    def _monte_carlo(self, means, cov, levels=None, samples=None, weights=None, num_fantasies=None, temperature=None, incumbents=None, level_fn=None, **kwargs):
        """Monte Carlo estimate of negative q-EI."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        if level_fn is None:
            level_fn = lambda outputs_old, **kwargs: tf.reduce_min(outputs_old)
        self.level_fn = level_fn
        self.temperature = temperature
<|end_body_0|>

<|body_start_1|>
        if level_fn is None:
            level_fn = self.level_fn
        with tf.name_scope('closed_form') as scope:
            if levels is None:
                levels = level_fn(means=means, **kwargs)
            resid = levels - means
            stddv = utils.tile_as(tf.sqrt(var), resid)
            upper_bounds = utils.safe_div(resid, stddv)
            probabilities = utils.normal_cdf(upper_bounds)
            return tf.negative(probabilities)
<|end_body_1|>

<|body_start_2|>
        if level_fn is None:
            level_fn = self.level_fn
        if temperature is None:
            temperature = self.temperature
        if num_fantasies is None:
            num_fantasies = self.num_fantasies
        with tf.name_scope('monte_carlo') as scope:
            if samples is None:
                samples = self.draw_samples(num_fantasies, means, cov, **kwargs)
            if levels is None:
                levels = level_fn(means=means, **kwargs)
            minima = tf.reduce_min(samples, axis=-1)
            if incumbents is not None:
                minima = tf.minimum(minima, incumbents)
            if temperature is None:
                improved = tf.cast(tf.less(minima, levels), minima.dtype)
            else:
                improved = utils.soft_less(minima, levels, temperature)
            estimate = self.reduce_samples(improved, weights, axis=-1)
            return tf.negative(estimate)
<|end_body_2|>

Solution:
class negative_pi:
    def __init__(self, level_fn=None, temperature=None, **kwargs):
        """Arguments level_fn : function returning the level w.r.t. improvement is measured temperature : temperature parameter for soft_less"""
        super().__init__(**kwargs)
        if level_fn is None:
            level_fn = lambda outputs_old, **kwargs: tf.reduce_min(outputs_old)
        self.level_fn = level_fn
        self.temperature = temperature

    def _closed_form(self, means, var, levels=None, level_fn=None, **kwargs):
        """Closed-form expression for negative, marginal PI."""
        if level_fn is None:
            level_fn = self.level_fn
        with tf.name_scope('closed_form') as scope:
            if levels is None:
                levels = level_fn(means=means, **kwargs)
            resid = levels - means
            stddv = utils.tile_as(tf.sqrt(var), resid)
            upper_bounds = utils.safe_div(resid, stddv)
            probabilities = utils.normal_cdf(upper_bounds)
            return tf.negative(probabilities)

    def _monte_carlo(self, means, cov, levels=None, samples=None, weights=None, num_fantasies=None, temperature=None, incumbents=None, level_fn=None, **kwargs):
        """Monte Carlo estimate of negative q-EI."""
        if level_fn is None:
            level_fn = self.level_fn
        if temperature is None:
            temperature = self.temperature
        if num_fantasies is None:
            num_fantasies = self.num_fantasies
        with tf.name_scope('monte_carlo') as scope:
            if samples is None:
                samples = self.draw_samples(num_fantasies, means, cov, **kwargs)
            if levels is None:
                levels = level_fn(means=means, **kwargs)
            minima = tf.reduce_min(samples, axis=-1)
            if incumbents is not None:
                minima = tf.minimum(minima, incumbents)
            if temperature is None:
                improved = tf.cast(tf.less(minima, levels), minima.dtype)
            else:
                improved = utils.soft_less(minima, levels, temperature)
            estimate = self.reduce_samples(improved, weights, axis=-1)
            return tf.negative(estimate)


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

Class description:
Implement the SalesRecordEnpoint class.

Method signatures and docstrings:
- def post(self): Post a sale_record
- def get(self, sale_id): Retrieve a single sales

Implement the Python class `SalesRecordEnpoint` described below.

Class description:
Implement the SalesRecordEnpoint class.

Method signatures and docstrings:
- def post(self): Post a sale_record
- def get(self, sale_id): Retrieve a single sales

<|skeleton|>
class SalesRecordEnpoint:

    def post(self):
        """Post a sale_record"""
        <|body_0|>

    def get(self, sale_id):
        """Retrieve a single sales"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = request.get_json()
        sale_attendant = get_jwt_identity()['email']
        name = InputValidator.valid_string(data['name'].strip())
        quantity_to_sell = InputValidator.valid_number(data['quantity'])
        payload = ['name', 'quantity']
        for item in data.keys():
            if item not in payload:
                return ({'message': f'The field {item} is not a valid field'}, 400)
        if Product.retrieve_product_by_name(name):
            product_on_sale = Product.retrieve_product_by_name(name)
            if quantity_to_sell <= product_on_sale['quantity']:
                total = product_on_sale['price'] * quantity_to_sell
                new_sale_record = Sales(quantity_to_sell, name, sale_attendant, total)
                added_sale_record = new_sale_record.save_record()
                Product.update_product(name, product_on_sale['category'], product_on_sale['price'], product_on_sale['quantity'] - quantity_to_sell, product_on_sale['description'], product_on_sale['prod_id'])
                new_quantity = product_on_sale['quantity'] - quantity_to_sell
                return ({'sale record': added_sale_record, 'message': f"The quantity of {product_on_sale['name']} has been updated new quantity is {new_quantity}"}, 200)
            return ({'message': f'The quantity you entered exceeds stoked quantity'}, 400)
        return ({'message': f'product {name} does not exist'}, 404)
<|end_body_0|>

<|body_start_1|>
        user = get_jwt_identity()['email']
        role = get_jwt_claims()['role']
        sales = Sales.retrieve_sales_by_id(sale_id)
        print(sales)
        if sales:
            if role == 'admin' or user == sales['sale_attendant']:
                return ({'sale_records': sales, 'message': 'Retrieved successfully'}, 200)
            return ({'message': 'You do not have authorization to access the sale record'}, 401)
        return ({'message': f'Sale of ID {sale_id} does not exist'}, 404)
<|end_body_1|>

Solution:
class SalesRecordEnpoint:
    def post(self):
        """Post a sale_record"""
        data = request.get_json()
        sale_attendant = get_jwt_identity()['email']
        name = InputValidator.valid_string(data['name'].strip())
        quantity_to_sell = InputValidator.valid_number(data['quantity'])
        payload = ['name', 'quantity']
        for item in data.keys():
            if item not in payload:
                return ({'message': f'The field {item} is not a valid field'}, 400)
        if Product.retrieve_product_by_name(name):
            product_on_sale = Product.retrieve_product_by_name(name)
            if quantity_to_sell <= product_on_sale['quantity']:
                total = product_on_sale['price'] * quantity_to_sell
                new_sale_record = Sales(quantity_to_sell, name, sale_attendant, total)
                added_sale_record = new_sale_record.save_record()
                Product.update_product(name, product_on_sale['category'], product_on_sale['price'], product_on_sale['quantity'] - quantity_to_sell, product_on_sale['description'], product_on_sale['prod_id'])
                new_quantity = product_on_sale['quantity'] - quantity_to_sell
                return ({'sale record': added_sale_record, 'message': f"The quantity of {product_on_sale['name']} has been updated new quantity is {new_quantity}"}, 200)
            return ({'message': f'The quantity you entered exceeds stoked quantity'}, 400)
        return ({'message': f'product {name} does not exist'}, 404)

    def get(self, sale_id):
        """Retrieve a single sales"""
        user = get_jwt_identity()['email']
        role = get_jwt_claims()['role']
        sales = Sales.retrieve_sales_by_id(sale_id)
        print(sales)
        if sales:
            if role == 'admin' or user == sales['sale_attendant']:
                return ({'sale_records': sales, 'message': 'Retrieved successfully'}, 200)
            return ({'message': 'You do not have authorization to access the sale record'}, 401)
        return ({'message': f'Sale of ID {sale_id} does not exist'}, 404)


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

Class description:
Region of interest feature map pooler that supports pooling from one or more feature maps.

Method signatures and docstrings:
- def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4): Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can
- def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`.

Implement the Python class `ROIPooler` described below.

Class description:
Region of interest feature map pooler that supports pooling from one or more feature maps.

Method signatures and docstrings:
- def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4): Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can
- def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`.

<|skeleton|>
class ROIPooler:
    """Region of interest feature map pooler that supports pooling from one or more feature maps."""

    def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4):
        """Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can"""
        <|body_0|>

    def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]):
        """Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        if isinstance(output_size, int):
            output_size = (output_size, output_size)
        assert len(output_size) == 2
        assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
        self.output_size = output_size
        if pooler_type == 'ROIAlign':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False) for scale in scales))
        elif pooler_type == 'ROIAlignV2':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True) for scale in scales))
        elif pooler_type == 'ROIPool':
            self.level_poolers = nn.ModuleList((RoIPool(output_size, spatial_scale=scale) for scale in scales))
        elif pooler_type == 'ROIAlignRotated':
            self.level_poolers = nn.ModuleList((ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) for scale in scales))
        else:
            raise ValueError('Unknown pooler type: {}'.format(pooler_type))
        min_level = -math.log2(scales[0])
        max_level = -math.log2(scales[-1])
        assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level)), 'Featuremap stride is not power of 2!'
        self.min_level = int(min_level)
        self.max_level = int(max_level)
        assert len(scales) == self.max_level - self.min_level + 1, '[ROIPooler] Sizes of input featuremaps do not form a pyramid!'
        assert 0 <= self.min_level and self.min_level <= self.max_level
        self.canonical_level = canonical_level
        assert canonical_box_size > 0
        self.canonical_box_size = canonical_box_size
<|end_body_0|>

<|body_start_1|>
        num_level_assignments = len(self.level_poolers)
        if not is_fx_tracing():
            torch._assert(isinstance(x, list) and isinstance(box_lists, list), 'Arguments to pooler must be lists')
        assert_fx_safe(len(x) == num_level_assignments, 'unequal value, num_level_assignments={}, but x is list of {} Tensors'.format(num_level_assignments, len(x)))
        assert_fx_safe(len(box_lists) == x[0].size(0), 'unequal value, x[0] batch dim 0 is {}, but box_list has length {}'.format(x[0].size(0), len(box_lists)))
        if len(box_lists) == 0:
            return _create_zeros(None, x[0].shape[1], *self.output_size, x[0])
        pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists)
        if num_level_assignments == 1:
            return self.level_poolers[0](x[0], pooler_fmt_boxes)
        level_assignments = assign_boxes_to_levels(box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level)
        num_channels = x[0].shape[1]
        output_size = self.output_size[0]
        output = _create_zeros(pooler_fmt_boxes, num_channels, output_size, output_size, x[0])
        for level, pooler in enumerate(self.level_poolers):
            inds = nonzero_tuple(level_assignments == level)[0]
            pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
            output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
        return output
<|end_body_1|>

Solution:
class ROIPooler:
    """Region of interest feature map pooler that supports pooling from one or more feature maps."""

    def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4):
        """Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can"""
        super().__init__()
        if isinstance(output_size, int):
            output_size = (output_size, output_size)
        assert len(output_size) == 2
        assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
        self.output_size = output_size
        if pooler_type == 'ROIAlign':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False) for scale in scales))
        elif pooler_type == 'ROIAlignV2':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True) for scale in scales))
        elif pooler_type == 'ROIPool':
            self.level_poolers = nn.ModuleList((RoIPool(output_size, spatial_scale=scale) for scale in scales))
        elif pooler_type == 'ROIAlignRotated':
            self.level_poolers = nn.ModuleList((ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) for scale in scales))
        else:
            raise ValueError('Unknown pooler type: {}'.format(pooler_type))
        min_level = -math.log2(scales[0])
        max_level = -math.log2(scales[-1])
        assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level)), 'Featuremap stride is not power of 2!'
        self.min_level = int(min_level)
        self.max_level = int(max_level)
        assert len(scales) == self.max_level - self.min_level + 1, '[ROIPooler] Sizes of input featuremaps do not form a pyramid!'
        assert 0 <= self.min_level and self.min_level <= self.max_level
        self.canonical_level = canonical_level
        assert canonical_box_size > 0
        self.canonical_box_size = canonical_box_size

    def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]):
        """Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`."""
        num_level_assignments = len(self.level_poolers)
        if not is_fx_tracing():
            torch._assert(isinstance(x, list) and isinstance(box_lists, list), 'Arguments to pooler must be lists')
        assert_fx_safe(len(x) == num_level_assignments, 'unequal value, num_level_assignments={}, but x is list of {} Tensors'.format(num_level_assignments, len(x)))
        assert_fx_safe(len(box_lists) == x[0].size(0), 'unequal value, x[0] batch dim 0 is {}, but box_list has length {}'.format(x[0].size(0), len(box_lists)))
        if len(box_lists) == 0:
            return _create_zeros(None, x[0].shape[1], *self.output_size, x[0])
        pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists)
        if num_level_assignments == 1:
            return self.level_poolers[0](x[0], pooler_fmt_boxes)
        level_assignments = assign_boxes_to_levels(box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level)
        num_channels = x[0].shape[1]
        output_size = self.output_size[0]
        output = _create_zeros(pooler_fmt_boxes, num_channels, output_size, output_size, x[0])
        for level, pooler in enumerate(self.level_poolers):
            inds = nonzero_tuple(level_assignments == level)[0]
            pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
            output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
        return output


Gnoisegen

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

Class description:
Implement the Noisy class.

Method signatures and docstrings:
- def Gnoisegen(self, x, snr): Generate Gaussian white noise according to the set SNR, return noisy speech :param x: clean speech signal :param snr: SNR [dB] :return y: noisy speech :return noise: gaussian white noise
- def SNR_singlech(self, I, In): calculate SNR of noisy speech signal :param I: clean speech siganl :param In: noisy speech siganl :return snr:
- def add_noisedata(self, s, data, fs, fs1, snr): 把任意的噪声数据按设定的信噪比叠加在纯净信号上，构成带噪语音 :param s: clean speech signal :param data: arbitrary noise data :param fs: clean signal sample frequency :param fs1: data sample frequency :param snr: SNR [dB] :return noise: noise scaled by the set SNR :return signal: noisy (size: n * 1)

Implement the Python class `Noisy` described below.

Class description:
Implement the Noisy class.

Method signatures and docstrings:
- def Gnoisegen(self, x, snr): Generate Gaussian white noise according to the set SNR, return noisy speech :param x: clean speech signal :param snr: SNR [dB] :return y: noisy speech :return noise: gaussian white noise
- def SNR_singlech(self, I, In): calculate SNR of noisy speech signal :param I: clean speech siganl :param In: noisy speech siganl :return snr:
- def add_noisedata(self, s, data, fs, fs1, snr): 把任意的噪声数据按设定的信噪比叠加在纯净信号上，构成带噪语音 :param s: clean speech signal :param data: arbitrary noise data :param fs: clean signal sample frequency :param fs1: data sample frequency :param snr: SNR [dB] :return noise: noise scaled by the set SNR :return signal: noisy (size: n * 1)

<|skeleton|>
class Noisy:

    def Gnoisegen(self, x, snr):
        """Generate Gaussian white noise according to the set SNR, return noisy speech :param x: clean speech signal :param snr: SNR [dB] :return y: noisy speech :return noise: gaussian white noise"""
        <|body_0|>

    def SNR_singlech(self, I, In):
        """calculate SNR of noisy speech signal :param I: clean speech siganl :param In: noisy speech siganl :return snr:"""
        <|body_1|>

    def add_noisedata(self, s, data, fs, fs1, snr):
        """把任意的噪声数据按设定的信噪比叠加在纯净信号上，构成带噪语音 :param s: clean speech signal :param data: arbitrary noise data :param fs: clean signal sample frequency :param fs1: data sample frequency :param snr: SNR [dB] :return noise: noise scaled by the set SNR :return signal: noisy (size: n * 1)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        noise = np.random.randn(x.size)
        Nx = len(x)
        signal_power = 1 / Nx * np.sum(x * x)
        noise_power = 1 / Nx * np.sum(noise * noise)
        noise_variance = signal_power / 10 ** (snr / 10)
        noise = np.sqrt(noise_variance / noise_power) * noise
        y = x + noise
        return (y, noise)
<|end_body_0|>

<|body_start_1|>
        I = I.reshape(-1, 1)
        In = In.reshape(-1, 1)
        Ps = np.sum((I - np.mean(I)) ** 2)
        Pn = np.sum((I - In) ** 2)
        snr = 10 * np.log10(Ps / Pn)
        return snr
<|end_body_1|>

<|body_start_2|>
        s = s.reshape(-1, 1)
        s = s - np.mean(s)
        sL = len(s)
        if fs != fs1:
            x = librosa.resample(data, fs, fs1)
        else:
            x = data
        x = x.reshape(-1, 1)
        x = x - np.mean(x)
        xL = len(x)
        if xL >= sL:
            x = x[0:sL]
        else:
            print('Warning noise length < signal length, padding with zero')
            x = np.concatenate((x, np.zeros(sL - xL)))
        Sr = snr
        Es = np.sum(x * x)
        Ev = np.sum(s * s)
        a = np.sqrt(Ev / Es / 10 ** (Sr / 10))
        noise = a * x
        signal = s + noise
        return (signal, noise)
<|end_body_2|>

Solution:
class Noisy:
    def Gnoisegen(self, x, snr):
        """Generate Gaussian white noise according to the set SNR, return noisy speech :param x: clean speech signal :param snr: SNR [dB] :return y: noisy speech :return noise: gaussian white noise"""
        noise = np.random.randn(x.size)
        Nx = len(x)
        signal_power = 1 / Nx * np.sum(x * x)
        noise_power = 1 / Nx * np.sum(noise * noise)
        noise_variance = signal_power / 10 ** (snr / 10)
        noise = np.sqrt(noise_variance / noise_power) * noise
        y = x + noise
        return (y, noise)

    def SNR_singlech(self, I, In):
        """calculate SNR of noisy speech signal :param I: clean speech siganl :param In: noisy speech siganl :return snr:"""
        I = I.reshape(-1, 1)
        In = In.reshape(-1, 1)
        Ps = np.sum((I - np.mean(I)) ** 2)
        Pn = np.sum((I - In) ** 2)
        snr = 10 * np.log10(Ps / Pn)
        return snr

    def add_noisedata(self, s, data, fs, fs1, snr):
        """把任意的噪声数据按设定的信噪比叠加在纯净信号上，构成带噪语音 :param s: clean speech signal :param data: arbitrary noise data :param fs: clean signal sample frequency :param fs1: data sample frequency :param snr: SNR [dB] :return noise: noise scaled by the set SNR :return signal: noisy (size: n * 1)"""
        s = s.reshape(-1, 1)
        s = s - np.mean(s)
        sL = len(s)
        if fs != fs1:
            x = librosa.resample(data, fs, fs1)
        else:
            x = data
        x = x.reshape(-1, 1)
        x = x - np.mean(x)
        xL = len(x)
        if xL >= sL:
            x = x[0:sL]
        else:
            print('Warning noise length < signal length, padding with zero')
            x = np.concatenate((x, np.zeros(sL - xL)))
        Sr = snr
        Es = np.sum(x * x)
        Ev = np.sum(s * s)
        a = np.sqrt(Ev / Es / 10 ** (Sr / 10))
        noise = a * x
        signal = s + noise
        return (signal, noise)