run

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

Class description:
Implement the Trainer class.

Method signatures and docstrings:
- def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels): Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_train instance of Dataset that holds the training data ds_validation instance of Dataset that holds the validation data stop_patience the training stops if the validation loss does not decrease for this number of epochs evaluation instance of Evaluation inputs placeholder for model inputs labels placeholder for model labels
- def _train_epoch(self, sess): trains for one epoch and prints the mean training loss to the commandline args: sess the tensorflow session that should be used
- def _valid_step(self, sess): run the validation and print evalution + mean validation loss to the commandline args: sess the tensorflow session that should be used
- def _should_stop(self): determine if training should stop according to stop_patience
- def run(self, sess, num_epochs=-1): run the training until num_epochs exceeds or the validation loss did not decrease for stop_patience epochs args: sess the tensorflow session that should be used num_epochs limit to the number of epochs, -1 means not limit

Implement the Python class `Trainer` described below.

Class description:
Implement the Trainer class.

Method signatures and docstrings:
- def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels): Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_train instance of Dataset that holds the training data ds_validation instance of Dataset that holds the validation data stop_patience the training stops if the validation loss does not decrease for this number of epochs evaluation instance of Evaluation inputs placeholder for model inputs labels placeholder for model labels
- def _train_epoch(self, sess): trains for one epoch and prints the mean training loss to the commandline args: sess the tensorflow session that should be used
- def _valid_step(self, sess): run the validation and print evalution + mean validation loss to the commandline args: sess the tensorflow session that should be used
- def _should_stop(self): determine if training should stop according to stop_patience
- def run(self, sess, num_epochs=-1): run the training until num_epochs exceeds or the validation loss did not decrease for stop_patience epochs args: sess the tensorflow session that should be used num_epochs limit to the number of epochs, -1 means not limit

<|skeleton|>
class Trainer:

    def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels):
        """Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_train instance of Dataset that holds the training data ds_validation instance of Dataset that holds the validation data stop_patience the training stops if the validation loss does not decrease for this number of epochs evaluation instance of Evaluation inputs placeholder for model inputs labels placeholder for model labels"""
        <|body_0|>

    def _train_epoch(self, sess):
        """trains for one epoch and prints the mean training loss to the commandline args: sess the tensorflow session that should be used"""
        <|body_1|>

    def _valid_step(self, sess):
        """run the validation and print evalution + mean validation loss to the commandline args: sess the tensorflow session that should be used"""
        <|body_2|>

    def _should_stop(self):
        """determine if training should stop according to stop_patience"""
        <|body_3|>

    def run(self, sess, num_epochs=-1):
        """run the training until num_epochs exceeds or the validation loss did not decrease for stop_patience epochs args: sess the tensorflow session that should be used num_epochs limit to the number of epochs, -1 means not limit"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self._train_op = optimizer.minimize(loss)
        self._loss = loss
        self._predictions = predictions
        self._ds_train = ds_train
        self._ds_validation = ds_validation
        self._stop_patience = stop_patience
        self._evaluation = evaluation
        self._validation_losses = []
        self._model_inputs = inputs
        self._model_labels = labels
        self._train_loss = []
        self._validation_f1 = []
        with tf.variable_scope('model', reuse=True):
            self._model_is_training = tf.get_variable('is_training', dtype=tf.bool, trainable=False)
<|end_body_0|>

<|body_start_1|>
        self._model_is_training = tf.identity(TRUE, name='is_training')
        dsIter = self._ds_train.__iter__()
        mean_loss = 0
        for i in range(dsIter._len):
            images, labels = next(dsIter)
            _, loss_value, prediction = sess.run([self._train_op, self._loss, self._predictions], feed_dict={self._model_inputs: images, self._model_labels: labels})
            mean_loss = mean_loss + loss_value
            self._evaluation.add_batch(prediction, labels)
        mean_loss = mean_loss / dsIter._len
        self._train_loss.append(mean_loss)
        print('training epoch:')
        self._evaluation.flush()
        print('mean loss: ', mean_loss)
        pass
<|end_body_1|>

<|body_start_2|>
        self._model_is_training = tf.identity(FALSE, name='is_validating')
        dsIter = self._ds_validation.__iter__()
        mean_loss = 0
        for i in range(dsIter._len):
            imgs, labels = next(dsIter)
            prediction, loss_value = sess.run([self._predictions, self._loss], feed_dict={self._model_inputs: imgs, self._model_labels: labels})
            mean_loss = mean_loss + loss_value
            self._evaluation.add_batch(prediction, labels)
        mean_loss = mean_loss / dsIter._len
        self._validation_losses.append(mean_loss)
        f1values = self._evaluation._measures[-1].values()
        f1mean = (f1values[0] + f1values[1]) / 2
        self._validation_f1.append(f1mean)
        print('validation step:')
        self._evaluation.flush()
        print('validation_loss: ', mean_loss)
        print('F1mean: ', f1mean)
        pass
<|end_body_2|>

<|body_start_3|>
        if len(self._validation_f1) < self._stop_patience:
            return False
        else:
            query = self._validation_f1[-self._stop_patience:]
            if any((query[i + 1] > query[i] for i in range(0, len(query) - 1))):
                return False
            else:
                return True
        pass
<|end_body_3|>

<|body_start_4|>
        self._valid_step(sess)
        i = 0
        while i < num_epochs or num_epochs == -1:
            print('epochs:{:d}'.format(i))
            self._train_epoch(sess)
            self._valid_step(sess)
            i += 1
            if self._should_stop():
                break
        print('end of run')
        print('start to plot')
<|end_body_4|>

Solution:
class Trainer:
    def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels):
        """Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_train instance of Dataset that holds the training data ds_validation instance of Dataset that holds the validation data stop_patience the training stops if the validation loss does not decrease for this number of epochs evaluation instance of Evaluation inputs placeholder for model inputs labels placeholder for model labels"""
        self._train_op = optimizer.minimize(loss)
        self._loss = loss
        self._predictions = predictions
        self._ds_train = ds_train
        self._ds_validation = ds_validation
        self._stop_patience = stop_patience
        self._evaluation = evaluation
        self._validation_losses = []
        self._model_inputs = inputs
        self._model_labels = labels
        self._train_loss = []
        self._validation_f1 = []
        with tf.variable_scope('model', reuse=True):
            self._model_is_training = tf.get_variable('is_training', dtype=tf.bool, trainable=False)

    def _train_epoch(self, sess):
        """trains for one epoch and prints the mean training loss to the commandline args: sess the tensorflow session that should be used"""
        self._model_is_training = tf.identity(TRUE, name='is_training')
        dsIter = self._ds_train.__iter__()
        mean_loss = 0
        for i in range(dsIter._len):
            images, labels = next(dsIter)
            _, loss_value, prediction = sess.run([self._train_op, self._loss, self._predictions], feed_dict={self._model_inputs: images, self._model_labels: labels})
            mean_loss = mean_loss + loss_value
            self._evaluation.add_batch(prediction, labels)
        mean_loss = mean_loss / dsIter._len
        self._train_loss.append(mean_loss)
        print('training epoch:')
        self._evaluation.flush()
        print('mean loss: ', mean_loss)
        pass

    def _valid_step(self, sess):
        """run the validation and print evalution + mean validation loss to the commandline args: sess the tensorflow session that should be used"""
        self._model_is_training = tf.identity(FALSE, name='is_validating')
        dsIter = self._ds_validation.__iter__()
        mean_loss = 0
        for i in range(dsIter._len):
            imgs, labels = next(dsIter)
            prediction, loss_value = sess.run([self._predictions, self._loss], feed_dict={self._model_inputs: imgs, self._model_labels: labels})
            mean_loss = mean_loss + loss_value
            self._evaluation.add_batch(prediction, labels)
        mean_loss = mean_loss / dsIter._len
        self._validation_losses.append(mean_loss)
        f1values = self._evaluation._measures[-1].values()
        f1mean = (f1values[0] + f1values[1]) / 2
        self._validation_f1.append(f1mean)
        print('validation step:')
        self._evaluation.flush()
        print('validation_loss: ', mean_loss)
        print('F1mean: ', f1mean)
        pass

    def _should_stop(self):
        """determine if training should stop according to stop_patience"""
        if len(self._validation_f1) < self._stop_patience:
            return False
        else:
            query = self._validation_f1[-self._stop_patience:]
            if any((query[i + 1] > query[i] for i in range(0, len(query) - 1))):
                return False
            else:
                return True
        pass

    def run(self, sess, num_epochs=-1):
        """run the training until num_epochs exceeds or the validation loss did not decrease for stop_patience epochs args: sess the tensorflow session that should be used num_epochs limit to the number of epochs, -1 means not limit"""
        self._valid_step(sess)
        i = 0
        while i < num_epochs or num_epochs == -1:
            print('epochs:{:d}'.format(i))
            self._train_epoch(sess)
            self._valid_step(sess)
            i += 1
            if self._should_stop():
                break
        print('end of run')
        print('start to plot')


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

Class description:
Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in experiments.

Method signatures and docstrings:
- def __init__(self): Initialises formatter.
- def split_data(self, df, valid_boundary=1315, test_boundary=1339): Splits data frame into training-validation-test data frames. This also calibrates scaling object, and transforms data for each split. Args: df: Source data frame to split. valid_boundary: Starting year for validation data test_boundary: Starting year for test data Returns: Tuple of transformed (train, valid, test) data.
- def set_scalers(self, df): Calibrates scalers using the data supplied. Args: df: Data to use to calibrate scalers.
- def transform_inputs(self, df): Performs feature transformations. This includes both feature engineering, preprocessing and normalisation. Args: df: Data frame to transform. Returns: Transformed data frame.
- def getcolumn_definition(self): "Returns formatted column definition in order expected by the TFT.

Implement the Python class `ElectricityFormatter` described below.

Class description:
Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in experiments.

Method signatures and docstrings:
- def __init__(self): Initialises formatter.
- def split_data(self, df, valid_boundary=1315, test_boundary=1339): Splits data frame into training-validation-test data frames. This also calibrates scaling object, and transforms data for each split. Args: df: Source data frame to split. valid_boundary: Starting year for validation data test_boundary: Starting year for test data Returns: Tuple of transformed (train, valid, test) data.
- def set_scalers(self, df): Calibrates scalers using the data supplied. Args: df: Data to use to calibrate scalers.
- def transform_inputs(self, df): Performs feature transformations. This includes both feature engineering, preprocessing and normalisation. Args: df: Data frame to transform. Returns: Transformed data frame.
- def getcolumn_definition(self): "Returns formatted column definition in order expected by the TFT.

<|skeleton|>
class ElectricityFormatter:
    """Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in experiments."""

    def __init__(self):
        """Initialises formatter."""
        <|body_0|>

    def split_data(self, df, valid_boundary=1315, test_boundary=1339):
        """Splits data frame into training-validation-test data frames. This also calibrates scaling object, and transforms data for each split. Args: df: Source data frame to split. valid_boundary: Starting year for validation data test_boundary: Starting year for test data Returns: Tuple of transformed (train, valid, test) data."""
        <|body_1|>

    def set_scalers(self, df):
        """Calibrates scalers using the data supplied. Args: df: Data to use to calibrate scalers."""
        <|body_2|>

    def transform_inputs(self, df):
        """Performs feature transformations. This includes both feature engineering, preprocessing and normalisation. Args: df: Data frame to transform. Returns: Transformed data frame."""
        <|body_3|>

    def getcolumn_definition(self):
        """"Returns formatted column definition in order expected by the TFT."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.identifiers = None
        self._real_scalers = None
        self._cat_scalers = None
        self._target_scaler = None
        self._num_classes_per_cat_input = None
        self._time_steps = get_fixed_params()['total_time_steps']
        self._num_encoder_steps = get_fixed_params()['num_encoder_steps']
<|end_body_0|>

<|body_start_1|>
        print_info('Formatting train-valid-test splits.')
        index = df['days_from_start']
        train = df.loc[index < valid_boundary]
        valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
        test = df.loc[index >= test_boundary - 7]
        self.set_scalers(train)
        return (self.transform_inputs(data) for data in [train, valid, test])
<|end_body_1|>

<|body_start_2|>
        print_info('Setting scalers with training data...')
        column_definitions = self.getcolumn_definition()
        id_column = get_single_col_by_input_type(InputTypes.ID, column_definitions)
        target_column = get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
        real_inputs = extract_cols_from_data_type(DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME})
        self.real_scalers = {}
        self.target_scaler = {}
        identifiers = []
        for identifier, sliced in df.groupby(id_column):
            if len(sliced) >= self._time_steps:
                data = sliced[real_inputs].values
                targets = sliced[[target_column]].values
                self.real_scalers[identifier] = sk_preprocessing.StandardScaler().fit(data)
                self.target_scaler[identifier] = sk_preprocessing.StandardScaler().fit(targets)
                identifiers.append(identifier)
        categorical_inputs = extract_cols_from_data_type(DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME})
        categorical_scalers = {}
        num_classes = []
        for col in categorical_inputs:
            srs = df[col].apply(str)
            categorical_scalers[col] = sk_preprocessing.LabelEncoder().fit(srs.values)
            num_classes.append(srs.nunique())
        self._cat_scalers = categorical_scalers
        self._num_classes_per_cat_input = num_classes
        self.identifiers = identifiers
<|end_body_2|>

<|body_start_3|>
        if self.real_scalers is None and self._cat_scalers is None:
            raise ValueError('Scalers have not been set!')
        column_definitions = self.getcolumn_definition()
        id_col = get_single_col_by_input_type(InputTypes.ID, column_definitions)
        real_inputs = extract_cols_from_data_type(DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME})
        categorical_inputs = extract_cols_from_data_type(DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME})
        df_list = []
        for identifier, sliced in df.groupby(id_col):
            if len(sliced) >= self._time_steps:
                sliced_copy = sliced.copy()
                sliced_copy[real_inputs] = self.real_scalers[identifier].transform(sliced_copy[real_inputs].values)
                df_list.append(sliced_copy)
        output = pd.concat(df_list, axis=0)
        for col in categorical_inputs:
            string_df = df[col].apply(str)
            output[col] = self._cat_scalers[col].transform(string_df)
        return output
<|end_body_3|>

<|body_start_4|>
        column_definition = self.column_definition

        def _check_single_column(input_type):
            length = len([tup for tup in column_definition if tup[2] == input_type])
            if length != 1:
                raise ValueError('Illegal number of inputs ({}) of type {}'.format(length, input_type))
        _check_single_column(InputTypes.ID)
        _check_single_column(InputTypes.TIME)
        identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
        time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
        real_inputs = [tup for tup in column_definition if tup[1] == DataTypes.REAL_VALUED and tup[2] not in {InputTypes.ID, InputTypes.TIME}]
        categorical_inputs = [tup for tup in column_definition if tup[1] == DataTypes.CATEGORICAL and tup[2] not in {InputTypes.ID, InputTypes.TIME}]
        return identifier + time + real_inputs + categorical_inputs
<|end_body_4|>

Solution:
class ElectricityFormatter:
    """Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in experiments."""

    def __init__(self):
        """Initialises formatter."""
        self.identifiers = None
        self._real_scalers = None
        self._cat_scalers = None
        self._target_scaler = None
        self._num_classes_per_cat_input = None
        self._time_steps = get_fixed_params()['total_time_steps']
        self._num_encoder_steps = get_fixed_params()['num_encoder_steps']

    def split_data(self, df, valid_boundary=1315, test_boundary=1339):
        """Splits data frame into training-validation-test data frames. This also calibrates scaling object, and transforms data for each split. Args: df: Source data frame to split. valid_boundary: Starting year for validation data test_boundary: Starting year for test data Returns: Tuple of transformed (train, valid, test) data."""
        print_info('Formatting train-valid-test splits.')
        index = df['days_from_start']
        train = df.loc[index < valid_boundary]
        valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
        test = df.loc[index >= test_boundary - 7]
        self.set_scalers(train)
        return (self.transform_inputs(data) for data in [train, valid, test])

    def set_scalers(self, df):
        """Calibrates scalers using the data supplied. Args: df: Data to use to calibrate scalers."""
        print_info('Setting scalers with training data...')
        column_definitions = self.getcolumn_definition()
        id_column = get_single_col_by_input_type(InputTypes.ID, column_definitions)
        target_column = get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
        real_inputs = extract_cols_from_data_type(DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME})
        self.real_scalers = {}
        self.target_scaler = {}
        identifiers = []
        for identifier, sliced in df.groupby(id_column):
            if len(sliced) >= self._time_steps:
                data = sliced[real_inputs].values
                targets = sliced[[target_column]].values
                self.real_scalers[identifier] = sk_preprocessing.StandardScaler().fit(data)
                self.target_scaler[identifier] = sk_preprocessing.StandardScaler().fit(targets)
                identifiers.append(identifier)
        categorical_inputs = extract_cols_from_data_type(DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME})
        categorical_scalers = {}
        num_classes = []
        for col in categorical_inputs:
            srs = df[col].apply(str)
            categorical_scalers[col] = sk_preprocessing.LabelEncoder().fit(srs.values)
            num_classes.append(srs.nunique())
        self._cat_scalers = categorical_scalers
        self._num_classes_per_cat_input = num_classes
        self.identifiers = identifiers

    def transform_inputs(self, df):
        """Performs feature transformations. This includes both feature engineering, preprocessing and normalisation. Args: df: Data frame to transform. Returns: Transformed data frame."""
        if self.real_scalers is None and self._cat_scalers is None:
            raise ValueError('Scalers have not been set!')
        column_definitions = self.getcolumn_definition()
        id_col = get_single_col_by_input_type(InputTypes.ID, column_definitions)
        real_inputs = extract_cols_from_data_type(DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME})
        categorical_inputs = extract_cols_from_data_type(DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME})
        df_list = []
        for identifier, sliced in df.groupby(id_col):
            if len(sliced) >= self._time_steps:
                sliced_copy = sliced.copy()
                sliced_copy[real_inputs] = self.real_scalers[identifier].transform(sliced_copy[real_inputs].values)
                df_list.append(sliced_copy)
        output = pd.concat(df_list, axis=0)
        for col in categorical_inputs:
            string_df = df[col].apply(str)
            output[col] = self._cat_scalers[col].transform(string_df)
        return output

    def getcolumn_definition(self):
        """"Returns formatted column definition in order expected by the TFT."""
        column_definition = self.column_definition

        def _check_single_column(input_type):
            length = len([tup for tup in column_definition if tup[2] == input_type])
            if length != 1:
                raise ValueError('Illegal number of inputs ({}) of type {}'.format(length, input_type))
        _check_single_column(InputTypes.ID)
        _check_single_column(InputTypes.TIME)
        identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
        time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
        real_inputs = [tup for tup in column_definition if tup[1] == DataTypes.REAL_VALUED and tup[2] not in {InputTypes.ID, InputTypes.TIME}]
        categorical_inputs = [tup for tup in column_definition if tup[1] == DataTypes.CATEGORICAL and tup[2] not in {InputTypes.ID, InputTypes.TIME}]
        return identifier + time + real_inputs + categorical_inputs


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

Class description:
Class for system tests for Ceilometer-Redis plugin.

Method signatures and docstrings:
- def add_remove_controller_kafka(self): Verify that the number of controllers can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one controller node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m
- def add_remove_compute_kafka(self): Verify that the number of computes can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one compute node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m

Implement the Python class `TestNodesKafkaPlugin` described below.

Class description:
Class for system tests for Ceilometer-Redis plugin.

Method signatures and docstrings:
- def add_remove_controller_kafka(self): Verify that the number of controllers can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one controller node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m
- def add_remove_compute_kafka(self): Verify that the number of computes can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one compute node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m

<|skeleton|>
class TestNodesKafkaPlugin:
    """Class for system tests for Ceilometer-Redis plugin."""

    def add_remove_controller_kafka(self):
        """Verify that the number of controllers can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one controller node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m"""
        <|body_0|>

    def add_remove_compute_kafka(self):
        """Verify that the number of computes can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one compute node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.env.revert_snapshot('deploy_kafka')
        target_node = {'slave-02': ['controller', self.settings.role_name]}
        self.helpers.remove_nodes_from_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.helpers.add_nodes_to_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
<|end_body_0|>

<|body_start_1|>
        self.env.revert_snapshot('deploy_kafka')
        target_node = {'slave-05': ['compute', 'cinder']}
        self.helpers.remove_nodes_from_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.helpers.add_nodes_to_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
<|end_body_1|>

Solution:
class TestNodesKafkaPlugin:
    """Class for system tests for Ceilometer-Redis plugin."""

    def add_remove_controller_kafka(self):
        """Verify that the number of controllers can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one controller node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m"""
        self.env.revert_snapshot('deploy_kafka')
        target_node = {'slave-02': ['controller', self.settings.role_name]}
        self.helpers.remove_nodes_from_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.helpers.add_nodes_to_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()

    def add_remove_compute_kafka(self):
        """Verify that the number of computes can scale up and down Scenario: 1. Revert snapshot with 5 deployed nodes in HA configuration 2. Remove one controller node and redeploy the cluster 3. Check that Kafka is running 4. Run OSTF 5. Add one compute node (return previous state) and redeploy the cluster 6. Check that Kafka is running 7. Run OSTF Duration 120m"""
        self.env.revert_snapshot('deploy_kafka')
        target_node = {'slave-05': ['compute', 'cinder']}
        self.helpers.remove_nodes_from_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.helpers.add_nodes_to_cluster(target_node)
        self.check_plugin_online()
        self.helpers.run_ostf()


detach

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

Class description:
DecoderState is a base class for models, used during translation for storing translation states.

Method signatures and docstrings:
- def detach(self): Detaches all Variables from the graph that created it, making it a leaf.
- def beam_update(self, idx, positions, beam_size): Update when beam advances.

Implement the Python class `DecoderState` described below.

Class description:
DecoderState is a base class for models, used during translation for storing translation states.

Method signatures and docstrings:
- def detach(self): Detaches all Variables from the graph that created it, making it a leaf.
- def beam_update(self, idx, positions, beam_size): Update when beam advances.

<|skeleton|>
class DecoderState:
    """DecoderState is a base class for models, used during translation for storing translation states."""

    def detach(self):
        """Detaches all Variables from the graph that created it, making it a leaf."""
        <|body_0|>

    def beam_update(self, idx, positions, beam_size):
        """Update when beam advances."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for h in self._all:
            if h is not None:
                h.detach_()
<|end_body_0|>

<|body_start_1|>
        for e in self._all:
            a, br, d = e.size()
            sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]
            sentStates.data.copy_(sentStates.data.index_select(1, positions))
<|end_body_1|>

Solution:
class DecoderState:
    """DecoderState is a base class for models, used during translation for storing translation states."""

    def detach(self):
        """Detaches all Variables from the graph that created it, making it a leaf."""
        for h in self._all:
            if h is not None:
                h.detach_()

    def beam_update(self, idx, positions, beam_size):
        """Update when beam advances."""
        for e in self._all:
            a, br, d = e.size()
            sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]
            sentStates.data.copy_(sentStates.data.index_select(1, positions))


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

Class description:
Class for meetup endpoints

Method signatures and docstrings:
- def __init__(self): Initialize the meetup class
- def post(self, current_user): Create meetup endpoint
- def get(self, current_user): Fetch all meetups

Implement the Python class `AllMeetups` described below.

Class description:
Class for meetup endpoints

Method signatures and docstrings:
- def __init__(self): Initialize the meetup class
- def post(self, current_user): Create meetup endpoint
- def get(self, current_user): Fetch all meetups

<|skeleton|>
class AllMeetups:
    """Class for meetup endpoints"""

    def __init__(self):
        """Initialize the meetup class"""
        <|body_0|>

    def post(self, current_user):
        """Create meetup endpoint"""
        <|body_1|>

    def get(self, current_user):
        """Fetch all meetups"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.parser = RequestParser()
        self.parser.add_argument('location', type=str, required=True, help='location field is missing')
        self.parser.add_argument('images', type=str, required=True, help='Image field is missing')
        self.parser.add_argument('topic', type=str, required=True, help='Topic field is missing')
        self.parser.add_argument('happeningOn', type=str, required=True, help='Date field is missing')
        self.parser.add_argument('tags', type=str, required=True, help='Tags field seems to be missing')
<|end_body_0|>

<|body_start_1|>
        userId = current_user['userid']
        args = self.parser.parse_args()
        args = request.get_json()
        location = args['location']
        images = args['images']
        topic = args['topic']
        happeningOn = args['happeningOn']
        tags = args['tags']
        if validate.valid_date(happeningOn):
            return validate.valid_date(happeningOn)
        if validate.valid_topic(topic):
            return validate.valid_topic(topic)
        if validate.valid_location(location):
            return validate.valid_location(location)
        if validate.valid_time(happeningOn):
            return validate.valid_time(happeningOn)
        meet = MeetupModels.repeat_meetup(self, location, happeningOn)
        meet = json.loads(meet)
        if meet:
            return ({'status': 409, 'error': 'a meetup with similar details exists'}, 409)
        meetup = MeetupModels(userId, location, images, topic, happeningOn, tags)
        newMeetup = meetup.create_meetup()
        newMeetup = json.loads(newMeetup)
        return ({'status': 201, 'meetup': newMeetup}, 201)
<|end_body_1|>

<|body_start_2|>
        meetups = MeetupModels.get_all(self)
        meetups = json.loads(meetups)
        if not meetups:
            return ({'status': 404, 'error': 'No meetups posted yet'}, 404)
        return ({'status': 200, 'data': meetups}, 200)
<|end_body_2|>

Solution:
class AllMeetups:
    """Class for meetup endpoints"""

    def __init__(self):
        """Initialize the meetup class"""
        self.parser = RequestParser()
        self.parser.add_argument('location', type=str, required=True, help='location field is missing')
        self.parser.add_argument('images', type=str, required=True, help='Image field is missing')
        self.parser.add_argument('topic', type=str, required=True, help='Topic field is missing')
        self.parser.add_argument('happeningOn', type=str, required=True, help='Date field is missing')
        self.parser.add_argument('tags', type=str, required=True, help='Tags field seems to be missing')

    def post(self, current_user):
        """Create meetup endpoint"""
        userId = current_user['userid']
        args = self.parser.parse_args()
        args = request.get_json()
        location = args['location']
        images = args['images']
        topic = args['topic']
        happeningOn = args['happeningOn']
        tags = args['tags']
        if validate.valid_date(happeningOn):
            return validate.valid_date(happeningOn)
        if validate.valid_topic(topic):
            return validate.valid_topic(topic)
        if validate.valid_location(location):
            return validate.valid_location(location)
        if validate.valid_time(happeningOn):
            return validate.valid_time(happeningOn)
        meet = MeetupModels.repeat_meetup(self, location, happeningOn)
        meet = json.loads(meet)
        if meet:
            return ({'status': 409, 'error': 'a meetup with similar details exists'}, 409)
        meetup = MeetupModels(userId, location, images, topic, happeningOn, tags)
        newMeetup = meetup.create_meetup()
        newMeetup = json.loads(newMeetup)
        return ({'status': 201, 'meetup': newMeetup}, 201)

    def get(self, current_user):
        """Fetch all meetups"""
        meetups = MeetupModels.get_all(self)
        meetups = json.loads(meetups)
        if not meetups:
            return ({'status': 404, 'error': 'No meetups posted yet'}, 404)
        return ({'status': 200, 'data': meetups}, 200)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def uniquePathsWithObstacles1(self, obstacleGrid: List[List[int]]) -> int: 典型DP
- def uniquePathsWithObstacles2(self, obstacleGrid: List[List[int]]) -> int: 二维数组压缩成一维数组 dp[j] = dp[j] + dp[j - 1] new dp[j] = old dp[j] + dp[j-1] current cell = top cell + left cell

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def uniquePathsWithObstacles1(self, obstacleGrid: List[List[int]]) -> int: 典型DP
- def uniquePathsWithObstacles2(self, obstacleGrid: List[List[int]]) -> int: 二维数组压缩成一维数组 dp[j] = dp[j] + dp[j - 1] new dp[j] = old dp[j] + dp[j-1] current cell = top cell + left cell

<|skeleton|>
class Solution:

    def uniquePathsWithObstacles1(self, obstacleGrid: List[List[int]]) -> int:
        """典型DP"""
        <|body_0|>

    def uniquePathsWithObstacles2(self, obstacleGrid: List[List[int]]) -> int:
        """二维数组压缩成一维数组 dp[j] = dp[j] + dp[j - 1] new dp[j] = old dp[j] + dp[j-1] current cell = top cell + left cell"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        m, n = (len(obstacleGrid), len(obstacleGrid[0]))
        dp = [[0 for _ in range(n)] for _ in range(m)]
        dp[0][0] = 0 if obstacleGrid[0][0] else 1
        for i in range(m):
            for j in range(n):
                if obstacleGrid[i][j]:
                    dp[i][j] = 0
                else:
                    if i:
                        dp[i][j] += dp[i - 1][j]
                    if j:
                        dp[i][j] += dp[i][j - 1]
        return dp[-1][-1]
<|end_body_0|>

<|body_start_1|>
        m, n = (len(obstacleGrid), len(obstacleGrid[0]))
        dp = [0] * n
        dp[0] = 0 if obstacleGrid[0][0] else 1
        for i in range(m):
            for j in range(n):
                if obstacleGrid[i][j]:
                    dp[j] = 0
                elif j and obstacleGrid[i][j - 1] == 0:
                    dp[j] += dp[j - 1]
        return dp[n - 1]
<|end_body_1|>

Solution:
class Solution:
    def uniquePathsWithObstacles1(self, obstacleGrid: List[List[int]]) -> int:
        """典型DP"""
        m, n = (len(obstacleGrid), len(obstacleGrid[0]))
        dp = [[0 for _ in range(n)] for _ in range(m)]
        dp[0][0] = 0 if obstacleGrid[0][0] else 1
        for i in range(m):
            for j in range(n):
                if obstacleGrid[i][j]:
                    dp[i][j] = 0
                else:
                    if i:
                        dp[i][j] += dp[i - 1][j]
                    if j:
                        dp[i][j] += dp[i][j - 1]
        return dp[-1][-1]

    def uniquePathsWithObstacles2(self, obstacleGrid: List[List[int]]) -> int:
        """二维数组压缩成一维数组 dp[j] = dp[j] + dp[j - 1] new dp[j] = old dp[j] + dp[j-1] current cell = top cell + left cell"""
        m, n = (len(obstacleGrid), len(obstacleGrid[0]))
        dp = [0] * n
        dp[0] = 0 if obstacleGrid[0][0] else 1
        for i in range(m):
            for j in range(n):
                if obstacleGrid[i][j]:
                    dp[j] = 0
                elif j and obstacleGrid[i][j - 1] == 0:
                    dp[j] += dp[j - 1]
        return dp[n - 1]


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

Class description:
The active dataset class takes care of concurrent reading of data from a dataset.

Method signatures and docstrings:
- def __init__(self, factory, task_queue, done_queue, batch_queue, args=None, kwargs=None): Args: factory: Class or factory function to use to open the dataset. filename: Filename of the dataset file to open. batch_queue: Queue on which to put the loaded batches. args: List of positional arguments to pass to the dataset factory following the dataset name. kwargs: Dictionary of keyword arguments to pass to the dataset factory following the dataset name.
- def run(self): Open dataset and start loading batches.

Implement the Python class `DatasetLoader` described below.

Class description:
The active dataset class takes care of concurrent reading of data from a dataset.

Method signatures and docstrings:
- def __init__(self, factory, task_queue, done_queue, batch_queue, args=None, kwargs=None): Args: factory: Class or factory function to use to open the dataset. filename: Filename of the dataset file to open. batch_queue: Queue on which to put the loaded batches. args: List of positional arguments to pass to the dataset factory following the dataset name. kwargs: Dictionary of keyword arguments to pass to the dataset factory following the dataset name.
- def run(self): Open dataset and start loading batches.

<|skeleton|>
class DatasetLoader:
    """The active dataset class takes care of concurrent reading of data from a dataset."""

    def __init__(self, factory, task_queue, done_queue, batch_queue, args=None, kwargs=None):
        """Args: factory: Class or factory function to use to open the dataset. filename: Filename of the dataset file to open. batch_queue: Queue on which to put the loaded batches. args: List of positional arguments to pass to the dataset factory following the dataset name. kwargs: Dictionary of keyword arguments to pass to the dataset factory following the dataset name."""
        <|body_0|>

    def run(self):
        """Open dataset and start loading batches."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.factory = factory
        self.task_queue = task_queue
        self.done_queue = done_queue
        self.batch_queue = batch_queue
        if args is None:
            self.args = []
        else:
            self.args = args
        if kwargs is None:
            self.kwargs = []
        else:
            self.kwargs = kwargs
<|end_body_0|>

<|body_start_1|>
        super().run()
        while True:
            filename = self.task_queue.get()
            if filename is None:
                break
            try:
                dataset = self.factory(filename, *self.args, **self.kwargs)
                if isinstance(dataset, Iterable):
                    for b in dataset:
                        self.batch_queue.put(b)
                elif hasattr(dataset, '__len__') and hasattr(dataset, '__getitem__'):
                    for i in range(len(dataset)):
                        self.batch_queue.put(dataset[i])
                else:
                    raise TypeError("Provided dataset is neither iterable nor does it implement '__getitem__' and '__len__' methods.")
            except Exception as e:
                _LOGGER.error('Error encountered in dataset loader: %s', e)
            self.done_queue.put(filename)
<|end_body_1|>

Solution:
class DatasetLoader:
    """The active dataset class takes care of concurrent reading of data from a dataset."""

    def __init__(self, factory, task_queue, done_queue, batch_queue, args=None, kwargs=None):
        """Args: factory: Class or factory function to use to open the dataset. filename: Filename of the dataset file to open. batch_queue: Queue on which to put the loaded batches. args: List of positional arguments to pass to the dataset factory following the dataset name. kwargs: Dictionary of keyword arguments to pass to the dataset factory following the dataset name."""
        super().__init__()
        self.factory = factory
        self.task_queue = task_queue
        self.done_queue = done_queue
        self.batch_queue = batch_queue
        if args is None:
            self.args = []
        else:
            self.args = args
        if kwargs is None:
            self.kwargs = []
        else:
            self.kwargs = kwargs

    def run(self):
        """Open dataset and start loading batches."""
        super().run()
        while True:
            filename = self.task_queue.get()
            if filename is None:
                break
            try:
                dataset = self.factory(filename, *self.args, **self.kwargs)
                if isinstance(dataset, Iterable):
                    for b in dataset:
                        self.batch_queue.put(b)
                elif hasattr(dataset, '__len__') and hasattr(dataset, '__getitem__'):
                    for i in range(len(dataset)):
                        self.batch_queue.put(dataset[i])
                else:
                    raise TypeError("Provided dataset is neither iterable nor does it implement '__getitem__' and '__len__' methods.")
            except Exception as e:
                _LOGGER.error('Error encountered in dataset loader: %s', e)
            self.done_queue.put(filename)


deserialize

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

Class description:
Implement the Codec class.

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

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

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

<|skeleton|>
class Codec:

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

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

<|end_skeleton|>

<|body_start_0|>
        ret = []

        def doSerialize(root):
            if root == None:
                ret.append('None')
                return
            ret.append(str(root.val))
            ret.append(str(len(root.children) if root.children else 0))
            for i in range(len(root.children)):
                doSerialize(root.children[i])
        doSerialize(root)
        print(','.join(ret))
        return ','.join(ret)
<|end_body_0|>

<|body_start_1|>
        token = [int(t) if t != 'None' else None for t in data.split(',')]
        print(token)

        def doDeserialize(token):
            if token[0] == None:
                token.pop(0)
                return None
            value = token.pop(0)
            child_len = token.pop(0)
            node = Node(value, [None] * child_len)
            for i in range(child_len):
                node.children[i] = doDeserialize(token)
            return node
        return doDeserialize(token)
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root: 'Node') -> str:
        """Encodes a tree to a single string. :type root: Node :rtype: str"""
        ret = []

        def doSerialize(root):
            if root == None:
                ret.append('None')
                return
            ret.append(str(root.val))
            ret.append(str(len(root.children) if root.children else 0))
            for i in range(len(root.children)):
                doSerialize(root.children[i])
        doSerialize(root)
        print(','.join(ret))
        return ','.join(ret)

    def deserialize(self, data: str) -> 'Node':
        """Decodes your encoded data to tree. :type data: str :rtype: Node"""
        token = [int(t) if t != 'None' else None for t in data.split(',')]
        print(token)

        def doDeserialize(token):
            if token[0] == None:
                token.pop(0)
                return None
            value = token.pop(0)
            child_len = token.pop(0)
            node = Node(value, [None] * child_len)
            for i in range(child_len):
                node.children[i] = doDeserialize(token)
            return node
        return doDeserialize(token)


save

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

Class description:
Form representing task model

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Overriden init method to have add project related data to fields
- def save(self, user, project, commit=True): Overriden save method to save virtual field which are not displayed to user

Implement the Python class `TaskForm` described below.

Class description:
Form representing task model

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Overriden init method to have add project related data to fields
- def save(self, user, project, commit=True): Overriden save method to save virtual field which are not displayed to user

<|skeleton|>
class TaskForm:
    """Form representing task model"""

    def __init__(self, *args, **kwargs):
        """Overriden init method to have add project related data to fields"""
        <|body_0|>

    def save(self, user, project, commit=True):
        """Overriden save method to save virtual field which are not displayed to user"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        project = kwargs.pop('project', None)
        super(self.__class__, self).__init__(*args, **kwargs)
        self.fields['parent'].queryset = Task.objects.filter(project=project)
        self.fields['type'].queryset = Type.objects.filter(is_project_type=True)
        self.fields['owner'].queryset = User.objects.filter(is_active=True, is_staff=False).order_by('username')
        try:
            self.fields['milestone'].queryset = project.milestone.filter(category=Milestone.ENGINEERING)
            self.fields['assigned_resources'].queryset = project.team.filter(is_active=True, is_staff=False).order_by('username')
        except BaseException:
            self.fields['assigned_resources'].queryset = User.objects.filter(is_active=True, is_staff=False).order_by('username')
<|end_body_0|>

<|body_start_1|>
        task = super(TaskForm, self).save(commit=False)
        task.project = project
        task.editor = user
        if not task.id:
            task.author = user
            task.created_at = datetime.now()
        if commit:
            task.save()

        def assign_resource(resource):
            return task.assigned_resources.add(resource)
        map(assign_resource, self.cleaned_data['assigned_resources'])
        return task
<|end_body_1|>

Solution:
class TaskForm:
    """Form representing task model"""

    def __init__(self, *args, **kwargs):
        """Overriden init method to have add project related data to fields"""
        project = kwargs.pop('project', None)
        super(self.__class__, self).__init__(*args, **kwargs)
        self.fields['parent'].queryset = Task.objects.filter(project=project)
        self.fields['type'].queryset = Type.objects.filter(is_project_type=True)
        self.fields['owner'].queryset = User.objects.filter(is_active=True, is_staff=False).order_by('username')
        try:
            self.fields['milestone'].queryset = project.milestone.filter(category=Milestone.ENGINEERING)
            self.fields['assigned_resources'].queryset = project.team.filter(is_active=True, is_staff=False).order_by('username')
        except BaseException:
            self.fields['assigned_resources'].queryset = User.objects.filter(is_active=True, is_staff=False).order_by('username')

    def save(self, user, project, commit=True):
        """Overriden save method to save virtual field which are not displayed to user"""
        task = super(TaskForm, self).save(commit=False)
        task.project = project
        task.editor = user
        if not task.id:
            task.author = user
            task.created_at = datetime.now()
        if commit:
            task.save()

        def assign_resource(resource):
            return task.assigned_resources.add(resource)
        map(assign_resource, self.cleaned_data['assigned_resources'])
        return task


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

Class description:
Base class for bitmasks.

Method signatures and docstrings:
- def get_name(self, val, level=0, strip=True): Given input value, returns names of all set bits, optionally of a given level
- def get_value(self, name): Get the numerical bit value of a given character name(s)
- def bad_value(self): Return bitmask value of all bits that indicate BAD in input bitmask
- def warn_value(self): Return bitmask value of all bits that indicate BAD in input bitmask

Implement the Python class `BitMask` described below.

Class description:
Base class for bitmasks.

Method signatures and docstrings:
- def get_name(self, val, level=0, strip=True): Given input value, returns names of all set bits, optionally of a given level
- def get_value(self, name): Get the numerical bit value of a given character name(s)
- def bad_value(self): Return bitmask value of all bits that indicate BAD in input bitmask
- def warn_value(self): Return bitmask value of all bits that indicate BAD in input bitmask

<|skeleton|>
class BitMask:
    """Base class for bitmasks."""

    def get_name(self, val, level=0, strip=True):
        """Given input value, returns names of all set bits, optionally of a given level"""
        <|body_0|>

    def get_value(self, name):
        """Get the numerical bit value of a given character name(s)"""
        <|body_1|>

    def bad_value(self):
        """Return bitmask value of all bits that indicate BAD in input bitmask"""
        <|body_2|>

    def warn_value(self):
        """Return bitmask value of all bits that indicate BAD in input bitmask"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        strflag = ''
        for ibit, name in enumerate(self.name):
            if np.uint64(val) & np.uint64(2 ** ibit) > 0 and (level == 0 or self.level == level):
                strflag = strflag + name + ','
        if strip:
            return strflag.strip(',')
        else:
            return strflag
<|end_body_0|>

<|body_start_1|>
        if type(name) is str:
            name = [name]
        bitval = np.uint64(0)
        for n in name:
            try:
                j = self.name.index(n.strip())
                bitval |= np.uint64(2 ** j)
            except:
                print('WARNING: undefined name: ', n)
        return bitval
<|end_body_1|>

<|body_start_2|>
        val = np.uint64(0)
        for i, level in enumerate(self.level):
            if level == 1:
                val = val | np.uint64(2 ** i)
        return val
<|end_body_2|>

<|body_start_3|>
        val = np.uint64(0)
        for i, level in enumerate(self.level):
            if level == 2:
                val = val | np.uint64(2 ** i)
        return val
<|end_body_3|>

Solution:
class BitMask:
    """Base class for bitmasks."""

    def get_name(self, val, level=0, strip=True):
        """Given input value, returns names of all set bits, optionally of a given level"""
        strflag = ''
        for ibit, name in enumerate(self.name):
            if np.uint64(val) & np.uint64(2 ** ibit) > 0 and (level == 0 or self.level == level):
                strflag = strflag + name + ','
        if strip:
            return strflag.strip(',')
        else:
            return strflag

    def get_value(self, name):
        """Get the numerical bit value of a given character name(s)"""
        if type(name) is str:
            name = [name]
        bitval = np.uint64(0)
        for n in name:
            try:
                j = self.name.index(n.strip())
                bitval |= np.uint64(2 ** j)
            except:
                print('WARNING: undefined name: ', n)
        return bitval

    def bad_value(self):
        """Return bitmask value of all bits that indicate BAD in input bitmask"""
        val = np.uint64(0)
        for i, level in enumerate(self.level):
            if level == 1:
                val = val | np.uint64(2 ** i)
        return val

    def warn_value(self):
        """Return bitmask value of all bits that indicate BAD in input bitmask"""
        val = np.uint64(0)
        for i, level in enumerate(self.level):
            if level == 2:
                val = val | np.uint64(2 ** i)
        return val


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

Class description:
Base class for XML parsing objects (e.g. PubMedArticle)

Method signatures and docstrings:
- def __init__(self, xml, root=None, *args, **kwargs): Instantiate with "xml" as string or bytes containing valid XML. Supply name of root element (string) to set virtual top level. (optional).
- def parse_xml(xml, root=None): Takes xml (str or bytes) and (optionally) a root element definition string. If root element defined, DOM object returned is rebased with this element as root. Args: xml (str or bytes) root (str): (optional) name of root element Returns: lxml document object.
- def _get(self, tag): Returns content of named XML element, or None if not found.

Implement the Python class `MetaPubObject` described below.

Class description:
Base class for XML parsing objects (e.g. PubMedArticle)

Method signatures and docstrings:
- def __init__(self, xml, root=None, *args, **kwargs): Instantiate with "xml" as string or bytes containing valid XML. Supply name of root element (string) to set virtual top level. (optional).
- def parse_xml(xml, root=None): Takes xml (str or bytes) and (optionally) a root element definition string. If root element defined, DOM object returned is rebased with this element as root. Args: xml (str or bytes) root (str): (optional) name of root element Returns: lxml document object.
- def _get(self, tag): Returns content of named XML element, or None if not found.

<|skeleton|>
class MetaPubObject:
    """Base class for XML parsing objects (e.g. PubMedArticle)"""

    def __init__(self, xml, root=None, *args, **kwargs):
        """Instantiate with "xml" as string or bytes containing valid XML. Supply name of root element (string) to set virtual top level. (optional)."""
        <|body_0|>

    def parse_xml(xml, root=None):
        """Takes xml (str or bytes) and (optionally) a root element definition string. If root element defined, DOM object returned is rebased with this element as root. Args: xml (str or bytes) root (str): (optional) name of root element Returns: lxml document object."""
        <|body_1|>

    def _get(self, tag):
        """Returns content of named XML element, or None if not found."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not xml:
            if xml == '':
                xml = 'empty'
            raise MetaPubError('Cannot build MetaPubObject; xml string was %s' % xml)
        self.xml = xml
        self.content = self.parse_xml(xml, root)
<|end_body_0|>

<|body_start_1|>
        if isinstance(xml, str) or isinstance(xml, bytes):
            dom = etree.XML(xml)
        else:
            dom = etree.XML(xml)
        if root:
            return dom.find(root)
        else:
            return dom
<|end_body_1|>

<|body_start_2|>
        elem = self.content.find(tag)
        if elem is not None:
            return elem.text
        return None
<|end_body_2|>

Solution:
class MetaPubObject:
    """Base class for XML parsing objects (e.g. PubMedArticle)"""

    def __init__(self, xml, root=None, *args, **kwargs):
        """Instantiate with "xml" as string or bytes containing valid XML. Supply name of root element (string) to set virtual top level. (optional)."""
        if not xml:
            if xml == '':
                xml = 'empty'
            raise MetaPubError('Cannot build MetaPubObject; xml string was %s' % xml)
        self.xml = xml
        self.content = self.parse_xml(xml, root)

    def parse_xml(xml, root=None):
        """Takes xml (str or bytes) and (optionally) a root element definition string. If root element defined, DOM object returned is rebased with this element as root. Args: xml (str or bytes) root (str): (optional) name of root element Returns: lxml document object."""
        if isinstance(xml, str) or isinstance(xml, bytes):
            dom = etree.XML(xml)
        else:
            dom = etree.XML(xml)
        if root:
            return dom.find(root)
        else:
            return dom

    def _get(self, tag):
        """Returns content of named XML element, or None if not found."""
        elem = self.content.find(tag)
        if elem is not None:
            return elem.text
        return None


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

Class description:
添加新的收车计划

Method signatures and docstrings:
- def on_change_cur_devs(self): 加开只能是 :return:
- def on_ok(self): 点击确定 :return:

Implement the Python class `AddNewBackPlan` described below.

Class description:
添加新的收车计划

Method signatures and docstrings:
- def on_change_cur_devs(self): 加开只能是 :return:
- def on_ok(self): 点击确定 :return:

<|skeleton|>
class AddNewBackPlan:
    """添加新的收车计划"""

    def on_change_cur_devs(self):
        """加开只能是 :return:"""
        <|body_0|>

    def on_ok(self):
        """点击确定 :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ids = self.cur_devs.ids
        rail_cache = {dev.id: dev.cur_rail.id for dev in self.cur_devs}
        old_dev_ids = self.plan_infos.mapped('cur_train_id.id')
        items = []
        for tmp_id in ids:
            if tmp_id not in old_dev_ids:
                items.append((0, 0, {'cur_train_id': tmp_id, 'rail': rail_cache[tmp_id], 'exchange_rail_time': utility.get_now_time_int_repr() + 10 * 60, 'type': 'back'}))
            else:
                for info in self.plan_infos:
                    if info.cur_train_id.id == tmp_id:
                        items.append((1, info.id, {'cur_train_id': tmp_id, 'rail': rail_cache[tmp_id], 'type': 'back'}))
                        break
        for tmp in self.plan_infos:
            if tmp.cur_train_id.id not in ids:
                items.append((2, tmp.id))
        self.plan_infos = items
<|end_body_0|>

<|body_start_1|>
        vals_list = []
        log_list = []
        location = self.env.user.cur_location
        if not location:
            raise exceptions.ValidationError('当前用户没有配置场段！请在右上角头像处配置!')
        back_train_need_min = location.receive_train_need_min
        for info in self.plan_infos:
            if not info.plan_time or not info.rail:
                raise exceptions.Warning('信息填写不完整')
            val = {'status': 'unpublish', 'train_id': info.cur_train_id.id, 'plan_back_location': location.id, 'date': self.plan_date, 'plan_back_time': info.plan_time, 'plan_back_rail': info.rail.id, 'exchange_rail_time': info.exchange_rail_time, 'plan_train_no': info.plan_train_no}
            if info.exchange_rail_time:
                val['plan_back_time'] = info.exchange_rail_time - back_train_need_min * 60
            else:
                val['plan_back_time'] = None
            vals_list.append(val)
            log = {'type': 'in_plan', 'train_dev': info.cur_train_id.train.id, 'operation': '新增收车计划'}
            log_list.append(log)
        self.env['metro_park_dispatch.train_back_plan'].create(vals_list)
        self.env['metro_park_dispatch.train_in_out_log'].create(log_list)
<|end_body_1|>

Solution:
class AddNewBackPlan:
    """添加新的收车计划"""

    def on_change_cur_devs(self):
        """加开只能是 :return:"""
        ids = self.cur_devs.ids
        rail_cache = {dev.id: dev.cur_rail.id for dev in self.cur_devs}
        old_dev_ids = self.plan_infos.mapped('cur_train_id.id')
        items = []
        for tmp_id in ids:
            if tmp_id not in old_dev_ids:
                items.append((0, 0, {'cur_train_id': tmp_id, 'rail': rail_cache[tmp_id], 'exchange_rail_time': utility.get_now_time_int_repr() + 10 * 60, 'type': 'back'}))
            else:
                for info in self.plan_infos:
                    if info.cur_train_id.id == tmp_id:
                        items.append((1, info.id, {'cur_train_id': tmp_id, 'rail': rail_cache[tmp_id], 'type': 'back'}))
                        break
        for tmp in self.plan_infos:
            if tmp.cur_train_id.id not in ids:
                items.append((2, tmp.id))
        self.plan_infos = items

    def on_ok(self):
        """点击确定 :return:"""
        vals_list = []
        log_list = []
        location = self.env.user.cur_location
        if not location:
            raise exceptions.ValidationError('当前用户没有配置场段！请在右上角头像处配置!')
        back_train_need_min = location.receive_train_need_min
        for info in self.plan_infos:
            if not info.plan_time or not info.rail:
                raise exceptions.Warning('信息填写不完整')
            val = {'status': 'unpublish', 'train_id': info.cur_train_id.id, 'plan_back_location': location.id, 'date': self.plan_date, 'plan_back_time': info.plan_time, 'plan_back_rail': info.rail.id, 'exchange_rail_time': info.exchange_rail_time, 'plan_train_no': info.plan_train_no}
            if info.exchange_rail_time:
                val['plan_back_time'] = info.exchange_rail_time - back_train_need_min * 60
            else:
                val['plan_back_time'] = None
            vals_list.append(val)
            log = {'type': 'in_plan', 'train_dev': info.cur_train_id.train.id, 'operation': '新增收车计划'}
            log_list.append(log)
        self.env['metro_park_dispatch.train_back_plan'].create(vals_list)
        self.env['metro_park_dispatch.train_in_out_log'].create(log_list)


groupAnagrams

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def groupAnagrams(self, strs): :type strs: List[str] :rtype: List[List[str]]
- def groupAnagrams2(self, strs): 计数法

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def groupAnagrams(self, strs): :type strs: List[str] :rtype: List[List[str]]
- def groupAnagrams2(self, strs): 计数法

<|skeleton|>
class Solution:

    def groupAnagrams(self, strs):
        """:type strs: List[str] :rtype: List[List[str]]"""
        <|body_0|>

    def groupAnagrams2(self, strs):
        """计数法"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        tmp = [Node(i, strs[i]) for i in range(len(strs))]
        tmp.sort()
        result = dict()
        for p in tmp:
            if p.string not in result:
                result[p.string] = [strs[p.id]]
            else:
                result[p.string].append(strs[p.id])
        return list(result.values())
<|end_body_0|>

<|body_start_1|>
        d = dict()
        for w in strs:
            word = getCountsStr(w)
            if word not in d:
                d[word] = [w]
            else:
                d[word].append(w)
        return list(d.values())
<|end_body_1|>

Solution:
class Solution:
    def groupAnagrams(self, strs):
        """:type strs: List[str] :rtype: List[List[str]]"""
        tmp = [Node(i, strs[i]) for i in range(len(strs))]
        tmp.sort()
        result = dict()
        for p in tmp:
            if p.string not in result:
                result[p.string] = [strs[p.id]]
            else:
                result[p.string].append(strs[p.id])
        return list(result.values())

    def groupAnagrams2(self, strs):
        """计数法"""
        d = dict()
        for w in strs:
            word = getCountsStr(w)
            if word not in d:
                d[word] = [w]
            else:
                d[word].append(w)
        return list(d.values())


build

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

Class description:
Implement the SupplierQubo class.

Method signatures and docstrings:
- def __init__(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None) -> None: Initializes the SupplierQubo inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory
- def build(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None): Bulds the qubo Args: inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory
- def construct_bqm(self): Construct BQM for the generalized set cover problem Args: Returns: Binary quadratic model instance
- def _post_process(self, solutions): Hack to return data formatted as expected

Implement the Python class `SupplierQubo` described below.

Class description:
Implement the SupplierQubo class.

Method signatures and docstrings:
- def __init__(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None) -> None: Initializes the SupplierQubo inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory
- def build(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None): Bulds the qubo Args: inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory
- def construct_bqm(self): Construct BQM for the generalized set cover problem Args: Returns: Binary quadratic model instance
- def _post_process(self, solutions): Hack to return data formatted as expected

<|skeleton|>
class SupplierQubo:

    def __init__(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None) -> None:
        """Initializes the SupplierQubo inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory"""
        <|body_0|>

    def build(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None):
        """Bulds the qubo Args: inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory"""
        <|body_1|>

    def construct_bqm(self):
        """Construct BQM for the generalized set cover problem Args: Returns: Binary quadratic model instance"""
        <|body_2|>

    def _post_process(self, solutions):
        """Hack to return data formatted as expected"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.inventory = None
        self.supplier_inventory = None
        self.indicators = None
        if inventory is not None and supplier_inventory is not None:
            self.build(inventory, supplier_inventory)
<|end_body_0|>

<|body_start_1|>
        print(f'Building QUBO')
        self.inventory = inventory
        self.supplier_inventory = supplier_inventory
        self.qubo = self.construct_bqm()
<|end_body_1|>

<|body_start_2|>
        self.indicators = []
        for i in range(len(self.supplier_inventory)):
            self.indicators.append([1 if self.inventory[a] in self.supplier_inventory[i] else 0 for a in range(len(self.inventory))])
        bqm = BinaryQuadraticModel(BINARY)
        self.x = [bqm.add_variable(f'x_{i + 1}', SupplierQubo.lagrange_a * sum(self.indicators[i]) + SupplierQubo.lagrange_b) for i in range(0, len(self.supplier_inventory))]
        y = []
        for a in range(1, len(self.inventory) + 1):
            y.append([bqm.add_variable(f'y_{(a, m)}', SupplierQubo.lagrange_a * (m ** 2 - 1)) for m in range(1, len(self.supplier_inventory) + 1)])
        for i in range(1, len(self.supplier_inventory) + 1):
            for j in range(i + 1, len(self.supplier_inventory) + 1):
                key = ('x_' + str(i), 'x_' + str(j))
                bqm.quadratic[key] = 2 * SupplierQubo.lagrange_a * np.dot(np.array(self.indicators[i - 1]), np.array(self.indicators[j - 1]))
        for m in range(1, len(self.supplier_inventory) + 1):
            for n in range(m + 1, len(self.supplier_inventory) + 1):
                for a in range(1, len(self.inventory) + 1):
                    key = ('y_(' + str(a) + ', ' + str(m) + ')', 'y_(' + str(a) + ', ' + str(n) + ')')
                    bqm.quadratic[key] = 2 * SupplierQubo.lagrange_a * (1 + m * n)
        for i in range(1, len(self.supplier_inventory) + 1):
            for m in range(1, len(self.supplier_inventory) + 1):
                for a in range(1, len(self.inventory) + 1):
                    key = ('x_' + str(i), 'y_(' + str(a) + ', ' + str(m) + ')')
                    bqm.quadratic[key] = -2 * SupplierQubo.lagrange_a * m * self.indicators[i - 1][a - 1]
        return bqm
<|end_body_2|>

<|body_start_3|>
        res = []
        for solution in solutions:
            res.append([solution[i] for i in self.x])
        return res
<|end_body_3|>

Solution:
class SupplierQubo:
    def __init__(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None) -> None:
        """Initializes the SupplierQubo inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory"""
        super().__init__()
        self.inventory = None
        self.supplier_inventory = None
        self.indicators = None
        if inventory is not None and supplier_inventory is not None:
            self.build(inventory, supplier_inventory)

    def build(self, inventory: list[int or str] or None, supplier_inventory: list[set[int or str]] or None):
        """Bulds the qubo Args: inventory (list): List of items we want for our inventory supplier_inventory (list of sets): List for each supplier their inventory"""
        print(f'Building QUBO')
        self.inventory = inventory
        self.supplier_inventory = supplier_inventory
        self.qubo = self.construct_bqm()

    def construct_bqm(self):
        """Construct BQM for the generalized set cover problem Args: Returns: Binary quadratic model instance"""
        self.indicators = []
        for i in range(len(self.supplier_inventory)):
            self.indicators.append([1 if self.inventory[a] in self.supplier_inventory[i] else 0 for a in range(len(self.inventory))])
        bqm = BinaryQuadraticModel(BINARY)
        self.x = [bqm.add_variable(f'x_{i + 1}', SupplierQubo.lagrange_a * sum(self.indicators[i]) + SupplierQubo.lagrange_b) for i in range(0, len(self.supplier_inventory))]
        y = []
        for a in range(1, len(self.inventory) + 1):
            y.append([bqm.add_variable(f'y_{(a, m)}', SupplierQubo.lagrange_a * (m ** 2 - 1)) for m in range(1, len(self.supplier_inventory) + 1)])
        for i in range(1, len(self.supplier_inventory) + 1):
            for j in range(i + 1, len(self.supplier_inventory) + 1):
                key = ('x_' + str(i), 'x_' + str(j))
                bqm.quadratic[key] = 2 * SupplierQubo.lagrange_a * np.dot(np.array(self.indicators[i - 1]), np.array(self.indicators[j - 1]))
        for m in range(1, len(self.supplier_inventory) + 1):
            for n in range(m + 1, len(self.supplier_inventory) + 1):
                for a in range(1, len(self.inventory) + 1):
                    key = ('y_(' + str(a) + ', ' + str(m) + ')', 'y_(' + str(a) + ', ' + str(n) + ')')
                    bqm.quadratic[key] = 2 * SupplierQubo.lagrange_a * (1 + m * n)
        for i in range(1, len(self.supplier_inventory) + 1):
            for m in range(1, len(self.supplier_inventory) + 1):
                for a in range(1, len(self.inventory) + 1):
                    key = ('x_' + str(i), 'y_(' + str(a) + ', ' + str(m) + ')')
                    bqm.quadratic[key] = -2 * SupplierQubo.lagrange_a * m * self.indicators[i - 1][a - 1]
        return bqm

    def _post_process(self, solutions):
        """Hack to return data formatted as expected"""
        res = []
        for solution in solutions:
            res.append([solution[i] for i in self.x])
        return res


forward

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

Class description:
Implement the TemporalConvNet class.

Method signatures and docstrings:
- def __init__(self, N, B, H, P, X, R, C, norm_type='gLN', causal=False, mask_nonlinear='relu'): Args: N: Number of filters in autoencoder B: Number of channels in bottleneck 1 × 1-conv block H: Number of channels in convolutional blocks P: Kernel size in convolutional blocks X: Number of convolutional blocks in each repeat R: Number of repeats C: Number of speakers norm_type: BN, gLN, cLN causal: causal or non-causal mask_nonlinear: use which non-linear function to generate mask
- def forward(self, mixture_w): Keep this API same with TasNet Args: mixture_w: [M, N, K], M is batch size returns: est_mask: [M, C, N, K]

Implement the Python class `TemporalConvNet` described below.

Class description:
Implement the TemporalConvNet class.

Method signatures and docstrings:
- def __init__(self, N, B, H, P, X, R, C, norm_type='gLN', causal=False, mask_nonlinear='relu'): Args: N: Number of filters in autoencoder B: Number of channels in bottleneck 1 × 1-conv block H: Number of channels in convolutional blocks P: Kernel size in convolutional blocks X: Number of convolutional blocks in each repeat R: Number of repeats C: Number of speakers norm_type: BN, gLN, cLN causal: causal or non-causal mask_nonlinear: use which non-linear function to generate mask
- def forward(self, mixture_w): Keep this API same with TasNet Args: mixture_w: [M, N, K], M is batch size returns: est_mask: [M, C, N, K]

<|skeleton|>
class TemporalConvNet:

    def __init__(self, N, B, H, P, X, R, C, norm_type='gLN', causal=False, mask_nonlinear='relu'):
        """Args: N: Number of filters in autoencoder B: Number of channels in bottleneck 1 × 1-conv block H: Number of channels in convolutional blocks P: Kernel size in convolutional blocks X: Number of convolutional blocks in each repeat R: Number of repeats C: Number of speakers norm_type: BN, gLN, cLN causal: causal or non-causal mask_nonlinear: use which non-linear function to generate mask"""
        <|body_0|>

    def forward(self, mixture_w):
        """Keep this API same with TasNet Args: mixture_w: [M, N, K], M is batch size returns: est_mask: [M, C, N, K]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(TemporalConvNet, self).__init__()
        self.C = C
        self.mask_nonlinear = mask_nonlinear
        layer_norm = ChannelwiseLayerNorm(N)
        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
        repeats = []
        for r in range(R):
            blocks = []
            for x in range(X):
                dilation = 2 ** x
                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]
            repeats += [nn.Sequential(*blocks)]
        temporal_conv_net = nn.Sequential(*repeats)
        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)
<|end_body_0|>

<|body_start_1|>
        M, N, K = mixture_w.size()
        score = self.network(mixture_w)
        score = score.view(M, self.C, N, K)
        if self.mask_nonlinear == 'softmax':
            est_mask = F.softmax(score, dim=1)
        elif self.mask_nonlinear == 'relu':
            est_mask = F.relu(score)
        else:
            raise ValueError('Unsupported mask non-linear function')
        return est_mask
<|end_body_1|>

Solution:
class TemporalConvNet:
    def __init__(self, N, B, H, P, X, R, C, norm_type='gLN', causal=False, mask_nonlinear='relu'):
        """Args: N: Number of filters in autoencoder B: Number of channels in bottleneck 1 × 1-conv block H: Number of channels in convolutional blocks P: Kernel size in convolutional blocks X: Number of convolutional blocks in each repeat R: Number of repeats C: Number of speakers norm_type: BN, gLN, cLN causal: causal or non-causal mask_nonlinear: use which non-linear function to generate mask"""
        super(TemporalConvNet, self).__init__()
        self.C = C
        self.mask_nonlinear = mask_nonlinear
        layer_norm = ChannelwiseLayerNorm(N)
        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
        repeats = []
        for r in range(R):
            blocks = []
            for x in range(X):
                dilation = 2 ** x
                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]
            repeats += [nn.Sequential(*blocks)]
        temporal_conv_net = nn.Sequential(*repeats)
        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)

    def forward(self, mixture_w):
        """Keep this API same with TasNet Args: mixture_w: [M, N, K], M is batch size returns: est_mask: [M, C, N, K]"""
        M, N, K = mixture_w.size()
        score = self.network(mixture_w)
        score = score.view(M, self.C, N, K)
        if self.mask_nonlinear == 'softmax':
            est_mask = F.softmax(score, dim=1)
        elif self.mask_nonlinear == 'relu':
            est_mask = F.relu(score)
        else:
            raise ValueError('Unsupported mask non-linear function')
        return est_mask


delete

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

Class description:
Check the REST Token and returns a list of all api_keys or the specified api_keys details

Method signatures and docstrings:
- def get(self, request, api_key_id=None, *args, **kwargs): Returns either a list of all api_keys with own access privileges or the members specified api_key :param request: :type request: :param api_key_id: :type api_key_id: :param args: :type args: :param kwargs: :type kwargs: :return: 200 / 403 :rtype:
- def put(self, request, *args, **kwargs): Creates an api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: 201 / 400 :rtype:
- def post(self, request, *args, **kwargs): Updates a api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: :rtype:
- def delete(self, request, *args, **kwargs): Deletes an api_key :param request: :param args: :param kwargs: :return: 200 / 400

Implement the Python class `APIKeyView` described below.

Class description:
Check the REST Token and returns a list of all api_keys or the specified api_keys details

Method signatures and docstrings:
- def get(self, request, api_key_id=None, *args, **kwargs): Returns either a list of all api_keys with own access privileges or the members specified api_key :param request: :type request: :param api_key_id: :type api_key_id: :param args: :type args: :param kwargs: :type kwargs: :return: 200 / 403 :rtype:
- def put(self, request, *args, **kwargs): Creates an api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: 201 / 400 :rtype:
- def post(self, request, *args, **kwargs): Updates a api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: :rtype:
- def delete(self, request, *args, **kwargs): Deletes an api_key :param request: :param args: :param kwargs: :return: 200 / 400

<|skeleton|>
class APIKeyView:
    """Check the REST Token and returns a list of all api_keys or the specified api_keys details"""

    def get(self, request, api_key_id=None, *args, **kwargs):
        """Returns either a list of all api_keys with own access privileges or the members specified api_key :param request: :type request: :param api_key_id: :type api_key_id: :param args: :type args: :param kwargs: :type kwargs: :return: 200 / 403 :rtype:"""
        <|body_0|>

    def put(self, request, *args, **kwargs):
        """Creates an api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: 201 / 400 :rtype:"""
        <|body_1|>

    def post(self, request, *args, **kwargs):
        """Updates a api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: :rtype:"""
        <|body_2|>

    def delete(self, request, *args, **kwargs):
        """Deletes an api_key :param request: :param args: :param kwargs: :return: 200 / 400"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if not api_key_id:
            api_keys = []
            for api_key in API_Key.objects.filter(user=request.user):
                api_keys.append({'id': api_key.id, 'title': api_key.title, 'read': api_key.read, 'write': api_key.write, 'restrict_to_secrets': api_key.restrict_to_secrets, 'allow_insecure_access': api_key.allow_insecure_access, 'active': api_key.active})
            return Response({'api_keys': api_keys}, status=status.HTTP_200_OK)
        else:
            try:
                api_key = API_Key.objects.get(id=api_key_id, user=request.user)
            except API_Key.DoesNotExist:
                return Response({'message': 'NO_PERMISSION_OR_NOT_EXIST', 'resource_id': api_key_id}, status=status.HTTP_400_BAD_REQUEST)
            response = {'id': api_key.id, 'title': api_key.title, 'public_key': api_key.public_key, 'private_key': api_key.private_key, 'private_key_nonce': api_key.private_key_nonce, 'secret_key': api_key.secret_key, 'secret_key_nonce': api_key.secret_key_nonce, 'read': api_key.read, 'write': api_key.write, 'restrict_to_secrets': api_key.restrict_to_secrets, 'allow_insecure_access': api_key.allow_insecure_access, 'active': api_key.active}
            return Response(response, status=status.HTTP_200_OK)
<|end_body_0|>

<|body_start_1|>
        serializer = CreateAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = API_Key.objects.create(user=request.user, title=str(serializer.validated_data.get('title')), public_key=str(serializer.validated_data.get('public_key')), private_key=str(serializer.validated_data.get('private_key')), private_key_nonce=str(serializer.validated_data.get('private_key_nonce')), secret_key=str(serializer.validated_data.get('secret_key')), secret_key_nonce=str(serializer.validated_data.get('secret_key_nonce')), user_private_key=str(serializer.validated_data.get('user_private_key')), user_private_key_nonce=str(serializer.validated_data.get('user_private_key_nonce')), user_secret_key=str(serializer.validated_data.get('user_secret_key')), user_secret_key_nonce=str(serializer.validated_data.get('user_secret_key_nonce')), verify_key=str(serializer.validated_data.get('verify_key')), read=serializer.validated_data.get('read'), write=serializer.validated_data.get('write'), restrict_to_secrets=serializer.validated_data.get('restrict_to_secrets'), allow_insecure_access=serializer.validated_data.get('allow_insecure_access'))
        return Response({'api_key_id': api_key.id}, status=status.HTTP_201_CREATED)
<|end_body_1|>

<|body_start_2|>
        serializer = UpdateAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = serializer.validated_data.get('api_key')
        title = serializer.validated_data.get('title')
        read = serializer.validated_data.get('read')
        write = serializer.validated_data.get('write')
        restrict_to_secrets = serializer.validated_data.get('restrict_to_secrets')
        allow_insecure_access = serializer.validated_data.get('allow_insecure_access')
        if title is not None:
            api_key.title = title
        if read is not None and api_key.read != read:
            api_key.read = read
            for token in api_key.tokens.all():
                token.read = read
                token.save()
        if write is not None and api_key.write != write:
            api_key.write = write
            for token in api_key.tokens.all():
                token.write = write
                token.save()
        if restrict_to_secrets is not None:
            api_key.restrict_to_secrets = restrict_to_secrets
        if allow_insecure_access is not None:
            api_key.allow_insecure_access = allow_insecure_access
        api_key.save()
        return Response(status=status.HTTP_200_OK)
<|end_body_2|>

<|body_start_3|>
        serializer = DeleteAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = serializer.validated_data.get('api_key')
        api_key.delete()
        return Response(status=status.HTTP_200_OK)
<|end_body_3|>

Solution:
class APIKeyView:
    """Check the REST Token and returns a list of all api_keys or the specified api_keys details"""

    def get(self, request, api_key_id=None, *args, **kwargs):
        """Returns either a list of all api_keys with own access privileges or the members specified api_key :param request: :type request: :param api_key_id: :type api_key_id: :param args: :type args: :param kwargs: :type kwargs: :return: 200 / 403 :rtype:"""
        if not api_key_id:
            api_keys = []
            for api_key in API_Key.objects.filter(user=request.user):
                api_keys.append({'id': api_key.id, 'title': api_key.title, 'read': api_key.read, 'write': api_key.write, 'restrict_to_secrets': api_key.restrict_to_secrets, 'allow_insecure_access': api_key.allow_insecure_access, 'active': api_key.active})
            return Response({'api_keys': api_keys}, status=status.HTTP_200_OK)
        else:
            try:
                api_key = API_Key.objects.get(id=api_key_id, user=request.user)
            except API_Key.DoesNotExist:
                return Response({'message': 'NO_PERMISSION_OR_NOT_EXIST', 'resource_id': api_key_id}, status=status.HTTP_400_BAD_REQUEST)
            response = {'id': api_key.id, 'title': api_key.title, 'public_key': api_key.public_key, 'private_key': api_key.private_key, 'private_key_nonce': api_key.private_key_nonce, 'secret_key': api_key.secret_key, 'secret_key_nonce': api_key.secret_key_nonce, 'read': api_key.read, 'write': api_key.write, 'restrict_to_secrets': api_key.restrict_to_secrets, 'allow_insecure_access': api_key.allow_insecure_access, 'active': api_key.active}
            return Response(response, status=status.HTTP_200_OK)

    def put(self, request, *args, **kwargs):
        """Creates an api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: 201 / 400 :rtype:"""
        serializer = CreateAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = API_Key.objects.create(user=request.user, title=str(serializer.validated_data.get('title')), public_key=str(serializer.validated_data.get('public_key')), private_key=str(serializer.validated_data.get('private_key')), private_key_nonce=str(serializer.validated_data.get('private_key_nonce')), secret_key=str(serializer.validated_data.get('secret_key')), secret_key_nonce=str(serializer.validated_data.get('secret_key_nonce')), user_private_key=str(serializer.validated_data.get('user_private_key')), user_private_key_nonce=str(serializer.validated_data.get('user_private_key_nonce')), user_secret_key=str(serializer.validated_data.get('user_secret_key')), user_secret_key_nonce=str(serializer.validated_data.get('user_secret_key_nonce')), verify_key=str(serializer.validated_data.get('verify_key')), read=serializer.validated_data.get('read'), write=serializer.validated_data.get('write'), restrict_to_secrets=serializer.validated_data.get('restrict_to_secrets'), allow_insecure_access=serializer.validated_data.get('allow_insecure_access'))
        return Response({'api_key_id': api_key.id}, status=status.HTTP_201_CREATED)

    def post(self, request, *args, **kwargs):
        """Updates a api_key :param request: :type request: :param args: :type args: :param kwargs: :type kwargs: :return: :rtype:"""
        serializer = UpdateAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = serializer.validated_data.get('api_key')
        title = serializer.validated_data.get('title')
        read = serializer.validated_data.get('read')
        write = serializer.validated_data.get('write')
        restrict_to_secrets = serializer.validated_data.get('restrict_to_secrets')
        allow_insecure_access = serializer.validated_data.get('allow_insecure_access')
        if title is not None:
            api_key.title = title
        if read is not None and api_key.read != read:
            api_key.read = read
            for token in api_key.tokens.all():
                token.read = read
                token.save()
        if write is not None and api_key.write != write:
            api_key.write = write
            for token in api_key.tokens.all():
                token.write = write
                token.save()
        if restrict_to_secrets is not None:
            api_key.restrict_to_secrets = restrict_to_secrets
        if allow_insecure_access is not None:
            api_key.allow_insecure_access = allow_insecure_access
        api_key.save()
        return Response(status=status.HTTP_200_OK)

    def delete(self, request, *args, **kwargs):
        """Deletes an api_key :param request: :param args: :param kwargs: :return: 200 / 400"""
        serializer = DeleteAPIKeySerializer(data=request.data, context=self.get_serializer_context())
        if not serializer.is_valid():
            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
        api_key = serializer.validated_data.get('api_key')
        api_key.delete()
        return Response(status=status.HTTP_200_OK)