text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Keras convolution layers and image transformation layers.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import six
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras import activations
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import constraints
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras import regularizers
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.engine.input_spec import InputSpec
  # imports for backwards namespace compatibility
  # pylint: disable=unused-import
  from tensorflow.python.keras.layers.pooling import AveragePooling1D
  from tensorflow.python.keras.layers.pooling import AveragePooling2D
  from tensorflow.python.keras.layers.pooling import AveragePooling3D
  from tensorflow.python.keras.layers.pooling import MaxPooling1D
  from tensorflow.python.keras.layers.pooling import MaxPooling2D
  from tensorflow.python.keras.layers.pooling import MaxPooling3D
  # pylint: enable=unused-import
  from tensorflow.python.keras.utils import conv_utils
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.util.tf_export import keras_export
  # pylint: disable=g-classes-have-attributes
  
  
  class Conv(Layer):
    """Abstract N-D convolution layer (private, used as implementation base).
  
    This layer creates a convolution kernel that is convolved
    (actually cross-correlated) with the layer input to produce a tensor of
    outputs. If `use_bias` is True (and a `bias_initializer` is provided),
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    Note: layer attributes cannot be modified after the layer has been called
    once (except the `trainable` attribute).
  
    Args:
      rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: An integer or tuple/list of n integers, specifying the
        length of the convolution window.
      strides: An integer or tuple/list of n integers,
        specifying the stride length of the convolution.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"`,  `"same"`, or `"causal"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros
        evenly to the left/right or up/down of the input such that output has the
        same height/width dimension as the input. `"causal"` results in causal
        (dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, ..., channels)` while `channels_first` corresponds to
        inputs with shape `(batch_size, channels, ...)`.
      dilation_rate: An integer or tuple/list of n integers, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      groups: A positive integer specifying the number of groups in which the
        input is split along the channel axis. Each group is convolved
        separately with `filters / groups` filters. The output is the
        concatenation of all the `groups` results along the channel axis.
        Input channels and `filters` must both be divisible by `groups`.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: An initializer for the convolution kernel. If None, the 
        default initializer (glorot_uniform) will be used. 
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer (zeros) will be used.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      kernel_constraint: Optional projection function to be applied to the
          kernel after being updated by an `Optimizer` (e.g. used to implement
          norm constraints or value constraints for layer weights). The function
          must take as input the unprojected variable and must return the
          projected variable (which must have the same shape). Constraints are
          not safe to use when doing asynchronous distributed training.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
    """
  
    def __init__(self,
                 rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 groups=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 trainable=True,
                 name=None,
                 conv_op=None,
                 **kwargs):
      super(Conv, self).__init__(
          trainable=trainable,
          name=name,
          activity_regularizer=regularizers.get(activity_regularizer),
          **kwargs)
      self.rank = rank
  
      if isinstance(filters, float):
        filters = int(filters)
      self.filters = filters
      self.groups = groups or 1
      self.kernel_size = conv_utils.normalize_tuple(
          kernel_size, rank, 'kernel_size')
      self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
      self.padding = conv_utils.normalize_padding(padding)
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.dilation_rate = conv_utils.normalize_tuple(
          dilation_rate, rank, 'dilation_rate')
  
      self.activation = activations.get(activation)
      self.use_bias = use_bias
  
      self.kernel_initializer = initializers.get(kernel_initializer)
      self.bias_initializer = initializers.get(bias_initializer)
      self.kernel_regularizer = regularizers.get(kernel_regularizer)
      self.bias_regularizer = regularizers.get(bias_regularizer)
      self.kernel_constraint = constraints.get(kernel_constraint)
      self.bias_constraint = constraints.get(bias_constraint)
      self.input_spec = InputSpec(min_ndim=self.rank + 2)
  
      self._validate_init()
      self._is_causal = self.padding == 'causal'
      self._channels_first = self.data_format == 'channels_first'
      self._tf_data_format = conv_utils.convert_data_format(
          self.data_format, self.rank + 2)
  
    def _validate_init(self):
      if self.filters is not None and self.filters % self.groups != 0:
        raise ValueError(
            'The number of filters must be evenly divisible by the number of '
            'groups. Received: groups={}, filters={}'.format(
                self.groups, self.filters))
  
      if not all(self.kernel_size):
        raise ValueError('The argument `kernel_size` cannot contain 0(s). '
                         'Received: %s' % (self.kernel_size,))
  
      if not all(self.strides):
        raise ValueError('The argument `strides` cannot contains 0(s). '
                         'Received: %s' % (self.strides,))
  
      if (self.padding == 'causal' and not isinstance(self,
                                                      (Conv1D, SeparableConv1D))):
        raise ValueError('Causal padding is only supported for `Conv1D`'
                         'and `SeparableConv1D`.')
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape)
      input_channel = self._get_input_channel(input_shape)
      if input_channel % self.groups != 0:
        raise ValueError(
            'The number of input channels must be evenly divisible by the number '
            'of groups. Received groups={}, but the input has {} channels '
            '(full input shape is {}).'.format(self.groups, input_channel,
                                               input_shape))
      kernel_shape = self.kernel_size + (input_channel // self.groups,
                                         self.filters)
  
      self.kernel = self.add_weight(
          name='kernel',
          shape=kernel_shape,
          initializer=self.kernel_initializer,
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint,
          trainable=True,
          dtype=self.dtype)
      if self.use_bias:
        self.bias = self.add_weight(
            name='bias',
            shape=(self.filters,),
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
            trainable=True,
            dtype=self.dtype)
      else:
        self.bias = None
      channel_axis = self._get_channel_axis()
      self.input_spec = InputSpec(min_ndim=self.rank + 2,
                                  axes={channel_axis: input_channel})
  
      # Convert Keras formats to TF native formats.
      if self.padding == 'causal':
        tf_padding = 'VALID'  # Causal padding handled in `call`.
      elif isinstance(self.padding, six.string_types):
        tf_padding = self.padding.upper()
      else:
        tf_padding = self.padding
      tf_dilations = list(self.dilation_rate)
      tf_strides = list(self.strides)
  
      tf_op_name = self.__class__.__name__
      if tf_op_name == 'Conv1D':
        tf_op_name = 'conv1d'  # Backwards compat.
  
      self._convolution_op = functools.partial(
          nn_ops.convolution_v2,
          strides=tf_strides,
          padding=tf_padding,
          dilations=tf_dilations,
          data_format=self._tf_data_format,
          name=tf_op_name)
      self.built = True
  
    def call(self, inputs):
      input_shape = inputs.shape
  
      if self._is_causal:  # Apply causal padding to inputs for Conv1D.
        inputs = array_ops.pad(inputs, self._compute_causal_padding(inputs))
  
      outputs = self._convolution_op(inputs, self.kernel)
  
      if self.use_bias:
        output_rank = outputs.shape.rank
        if self.rank == 1 and self._channels_first:
          # nn.bias_add does not accept a 1D input tensor.
          bias = array_ops.reshape(self.bias, (1, self.filters, 1))
          outputs += bias
        else:
          # Handle multiple batch dimensions.
          if output_rank is not None and output_rank > 2 + self.rank:
  
            def _apply_fn(o):
              return nn.bias_add(o, self.bias, data_format=self._tf_data_format)
  
            outputs = conv_utils.squeeze_batch_dims(
                outputs, _apply_fn, inner_rank=self.rank + 1)
          else:
            outputs = nn.bias_add(
                outputs, self.bias, data_format=self._tf_data_format)
  
      if not context.executing_eagerly():
        # Infer the static output shape:
        out_shape = self.compute_output_shape(input_shape)
        outputs.set_shape(out_shape)
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
    def _spatial_output_shape(self, spatial_input_shape):
      return [
          conv_utils.conv_output_length(
              length,
              self.kernel_size[i],
              padding=self.padding,
              stride=self.strides[i],
              dilation=self.dilation_rate[i])
          for i, length in enumerate(spatial_input_shape)
      ]
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      batch_rank = len(input_shape) - self.rank - 1
      if self.data_format == 'channels_last':
        return tensor_shape.TensorShape(
            input_shape[:batch_rank]
            + self._spatial_output_shape(input_shape[batch_rank:-1])
            + [self.filters])
      else:
        return tensor_shape.TensorShape(
            input_shape[:batch_rank] + [self.filters] +
            self._spatial_output_shape(input_shape[batch_rank + 1:]))
  
    def _recreate_conv_op(self, inputs):  # pylint: disable=unused-argument
      return False
  
    def get_config(self):
      config = {
          'filters':
              self.filters,
          'kernel_size':
              self.kernel_size,
          'strides':
              self.strides,
          'padding':
              self.padding,
          'data_format':
              self.data_format,
          'dilation_rate':
              self.dilation_rate,
          'groups':
              self.groups,
          'activation':
              activations.serialize(self.activation),
          'use_bias':
              self.use_bias,
          'kernel_initializer':
              initializers.serialize(self.kernel_initializer),
          'bias_initializer':
              initializers.serialize(self.bias_initializer),
          'kernel_regularizer':
              regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint':
              constraints.serialize(self.kernel_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    def _compute_causal_padding(self, inputs):
      """Calculates padding for 'causal' option for 1-d conv layers."""
      left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)
      if getattr(inputs.shape, 'ndims', None) is None:
        batch_rank = 1
      else:
        batch_rank = len(inputs.shape) - 2
      if self.data_format == 'channels_last':
        causal_padding = [[0, 0]] * batch_rank + [[left_pad, 0], [0, 0]]
      else:
        causal_padding = [[0, 0]] * batch_rank + [[0, 0], [left_pad, 0]]
      return causal_padding
  
    def _get_channel_axis(self):
      if self.data_format == 'channels_first':
        return -1 - self.rank
      else:
        return -1
  
    def _get_input_channel(self, input_shape):
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs '
                         'should be defined. Found `None`.')
      return int(input_shape[channel_axis])
  
    def _get_padding_op(self):
      if self.padding == 'causal':
        op_padding = 'valid'
      else:
        op_padding = self.padding
      if not isinstance(op_padding, (list, tuple)):
        op_padding = op_padding.upper()
      return op_padding
  
  
  @keras_export('keras.layers.Conv1D', 'keras.layers.Convolution1D')
  class Conv1D(Conv):
    """1D convolution layer (e.g. temporal convolution).
  
    This layer creates a convolution kernel that is convolved
    with the layer input over a single spatial (or temporal) dimension
    to produce a tensor of outputs.
    If `use_bias` is True, a bias vector is created and added to the outputs.
    Finally, if `activation` is not `None`,
    it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide an `input_shape` argument
    (tuple of integers or `None`, e.g.
    `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
    or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
  
    Examples:
  
    >>> # The inputs are 128-length vectors with 10 timesteps, and the batch size
    >>> # is 4.
    >>> input_shape = (4, 10, 128)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv1D(
    ... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 8, 32)
  
    >>> # With extended batch shape [4, 7] (e.g. weather data where batch
    >>> # dimensions correspond to spatial location and the third dimension
    >>> # corresponds to time.)
    >>> input_shape = (4, 7, 10, 128)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv1D(
    ... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
    >>> print(y.shape)
    (4, 7, 8, 32)
  
    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of a single integer,
        specifying the length of the 1D convolution window.
      strides: An integer or tuple/list of a single integer,
        specifying the stride length of the convolution.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"`, `"same"` or `"causal"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
        `"causal"` results in causal (dilated) convolutions, e.g. `output[t]`
        does not depend on `input[t+1:]`. Useful when modeling temporal data
        where the model should not violate the temporal order.
        See [WaveNet: A Generative Model for Raw Audio, section
          2.1](https://arxiv.org/abs/1609.03499).
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
      dilation_rate: an integer or tuple/list of a single integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      groups: A positive integer specifying the number of groups in which the
        input is split along the channel axis. Each group is convolved
        separately with `filters / groups` filters. The output is the
        concatenation of all the `groups` results along the channel axis.
        Input channels and `filters` must both be divisible by `groups`.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (
        see `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (
        see `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation") (
        see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      3+D tensor with shape: `batch_shape + (steps, input_dim)`
  
    Output shape:
      3+D tensor with shape: `batch_shape + (new_steps, filters)`
        `steps` value might have changed due to padding or strides.
  
    Returns:
      A tensor of rank 3 representing
      `activation(conv1d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: when both `strides > 1` and `dilation_rate > 1`.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format='channels_last',
                 dilation_rate=1,
                 groups=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv1D, self).__init__(
          rank=1,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          groups=groups,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
  
  @keras_export('keras.layers.Conv2D', 'keras.layers.Convolution2D')
  class Conv2D(Conv):
    """2D convolution layer (e.g. spatial convolution over images).
  
    This layer creates a convolution kernel that is convolved
    with the layer input to produce a tensor of
    outputs. If `use_bias` is True,
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
    in `data_format="channels_last"`. You can use `None` when
    a dimension has variable size.
  
    Examples:
  
    >>> # The inputs are 28x28 RGB images with `channels_last` and the batch
    >>> # size is 4.
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 26, 26, 2)
  
    >>> # With `dilation_rate` as 2.
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', dilation_rate=2, input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 24, 24, 2)
  
    >>> # With `padding` as "same".
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 28, 28, 2)
  
    >>> # With extended batch shape [4, 7]:
    >>> input_shape = (4, 7, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
    >>> print(y.shape)
    (4, 7, 26, 26, 2)
  
  
    Args:
      filters: Integer, the dimensionality of the output space (i.e. the number of
        output filters in the convolution).
      kernel_size: An integer or tuple/list of 2 integers, specifying the height
        and width of the 2D convolution window. Can be a single integer to specify
        the same value for all spatial dimensions.
      strides: An integer or tuple/list of 2 integers, specifying the strides of
        the convolution along the height and width. Can be a single integer to
        specify the same value for all spatial dimensions. Specifying any stride
        value != 1 is incompatible with specifying any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs. `channels_last` corresponds
        to inputs with shape `(batch_size, height, width, channels)` while
        `channels_first` corresponds to inputs with shape `(batch_size, channels,
        height, width)`. It defaults to the `image_data_format` value found in
        your Keras config file at `~/.keras/keras.json`. If you never set it, then
        it will be `channels_last`.
      dilation_rate: an integer or tuple/list of 2 integers, specifying the
        dilation rate to use for dilated convolution. Can be a single integer to
        specify the same value for all spatial dimensions. Currently, specifying
        any `dilation_rate` value != 1 is incompatible with specifying any stride
        value != 1.
      groups: A positive integer specifying the number of groups in which the
        input is split along the channel axis. Each group is convolved separately
        with `filters / groups` filters. The output is the concatenation of all
        the `groups` results along the channel axis. Input channels and `filters`
        must both be divisible by `groups`.
      activation: Activation function to use. If you don't specify anything, no
        activation is applied (see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (see
        `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (see
        `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to the `kernel` weights
        matrix (see `keras.regularizers`). 
      bias_regularizer: Regularizer function applied to the bias vector (see
        `keras.regularizers`). 
      activity_regularizer: Regularizer function applied to the output of the
        layer (its "activation") (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (see
        `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (see
        `keras.constraints`).
    Input shape:
      4+D tensor with shape: `batch_shape + (channels, rows, cols)` if
        `data_format='channels_first'`
      or 4+D tensor with shape: `batch_shape + (rows, cols, channels)` if
        `data_format='channels_last'`.
    Output shape:
      4+D tensor with shape: `batch_shape + (filters, new_rows, new_cols)` if
      `data_format='channels_first'` or 4+D tensor with shape: `batch_shape +
        (new_rows, new_cols, filters)` if `data_format='channels_last'`.  `rows`
        and `cols` values might have changed due to padding.
  
    Returns:
      A tensor of rank 4+ representing
      `activation(conv2d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is `"causal"`.
      ValueError: when both `strides > 1` and `dilation_rate > 1`.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 groups=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv2D, self).__init__(
          rank=2,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          groups=groups,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
  
  @keras_export('keras.layers.Conv3D', 'keras.layers.Convolution3D')
  class Conv3D(Conv):
    """3D convolution layer (e.g. spatial convolution over volumes).
  
    This layer creates a convolution kernel that is convolved
    with the layer input to produce a tensor of
    outputs. If `use_bias` is True,
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
    with a single channel,
    in `data_format="channels_last"`.
  
    Examples:
  
    >>> # The inputs are 28x28x28 volumes with a single channel, and the
    >>> # batch size is 4
    >>> input_shape =(4, 28, 28, 28, 1)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv3D(
    ... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 26, 26, 26, 2)
  
    >>> # With extended batch shape [4, 7], e.g. a batch of 4 videos of 3D frames,
    >>> # with 7 frames per video.
    >>> input_shape = (4, 7, 28, 28, 28, 1)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv3D(
    ... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
    >>> print(y.shape)
    (4, 7, 26, 26, 26, 2)
  
    Args:
      filters: Integer, the dimensionality of the output space (i.e. the number of
        output filters in the convolution).
      kernel_size: An integer or tuple/list of 3 integers, specifying the depth,
        height and width of the 3D convolution window. Can be a single integer to
        specify the same value for all spatial dimensions.
      strides: An integer or tuple/list of 3 integers, specifying the strides of
        the convolution along each spatial dimension. Can be a single integer to
        specify the same value for all spatial dimensions. Specifying any stride
        value != 1 is incompatible with specifying any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs. `channels_last` corresponds
        to inputs with shape `batch_shape + (spatial_dim1, spatial_dim2,
        spatial_dim3, channels)` while `channels_first` corresponds to inputs with
        shape `batch_shape + (channels, spatial_dim1, spatial_dim2,
        spatial_dim3)`. It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`. If you never set it, then it
        will be "channels_last".
      dilation_rate: an integer or tuple/list of 3 integers, specifying the
        dilation rate to use for dilated convolution. Can be a single integer to
        specify the same value for all spatial dimensions. Currently, specifying
        any `dilation_rate` value != 1 is incompatible with specifying any stride
        value != 1.
      groups: A positive integer specifying the number of groups in which the
        input is split along the channel axis. Each group is convolved separately
        with `filters / groups` filters. The output is the concatenation of all
        the `groups` results along the channel axis. Input channels and `filters`
        must both be divisible by `groups`.
      activation: Activation function to use. If you don't specify anything, no
        activation is applied (see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (see
        `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (see
        `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to the `kernel` weights
        matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (see
        `keras.regularizers`).
      activity_regularizer: Regularizer function applied to the output of the
        layer (its "activation") (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (see
        `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (see
        `keras.constraints`).
    Input shape:
      5+D tensor with shape: `batch_shape + (channels, conv_dim1, conv_dim2,
        conv_dim3)` if data_format='channels_first'
      or 5+D tensor with shape: `batch_shape + (conv_dim1, conv_dim2, conv_dim3,
        channels)` if data_format='channels_last'.
    Output shape:
      5+D tensor with shape: `batch_shape + (filters, new_conv_dim1,
        new_conv_dim2, new_conv_dim3)` if data_format='channels_first'
      or 5+D tensor with shape: `batch_shape + (new_conv_dim1, new_conv_dim2,
        new_conv_dim3, filters)` if data_format='channels_last'. `new_conv_dim1`,
        `new_conv_dim2` and `new_conv_dim3` values might have changed due to
        padding.
  
    Returns:
      A tensor of rank 5+ representing
      `activation(conv3d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides > 1` and `dilation_rate > 1`.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1, 1),
                 groups=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv3D, self).__init__(
          rank=3,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          groups=groups,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
  
  @keras_export('keras.layers.Conv1DTranspose',
                'keras.layers.Convolution1DTranspose')
  class Conv1DTranspose(Conv1D):
    """Transposed convolution layer (sometimes called Deconvolution).
  
    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 3)` for data with 128 time steps and 3 channels.
  
    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer length of the 1D convolution window.
      strides: An integer specifying the stride of the convolution along the
        time dimension. Specifying a stride value != 1 is incompatible with
        specifying a `dilation_rate` value != 1. Defaults to 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      output_padding: An integer specifying the amount of padding along
        the time dimension of the output tensor.
        The amount of output padding must be lower than the stride.
        If set to `None` (default), the output shape is inferred.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, length, channels)` while `channels_first` corresponds to
        inputs with shape `(batch_size, channels, length)`.
      dilation_rate: an integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying a `dilation_rate` value != 1 is
        incompatible with specifying a stride value != 1.
        Also dilation rate larger than 1 is not currently supported.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (
        see `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (
        see `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation") (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      3D tensor with shape:
      `(batch_size, steps, channels)`
  
    Output shape:
      3D tensor with shape:
      `(batch_size, new_steps, filters)`
      If `output_padding` is specified:
      ```
      new_timesteps = ((timesteps - 1) * strides + kernel_size -
      2 * padding + output_padding)
      ```
  
    Returns:
      A tensor of rank 3 representing
      `activation(conv1dtranspose(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
  
    References:
      - [A guide to convolution arithmetic for deep learning](
        https://arxiv.org/abs/1603.07285v1)
      - [Deconvolutional Networks](
        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 output_padding=None,
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv1DTranspose, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
      self.output_padding = output_padding
      if self.output_padding is not None:
        self.output_padding = conv_utils.normalize_tuple(
            self.output_padding, 1, 'output_padding')
        for stride, out_pad in zip(self.strides, self.output_padding):
          if out_pad >= stride:
            raise ValueError('Stride ' + str(self.strides) + ' must be '
                             'greater than output padding ' +
                             str(self.output_padding))
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape)
      if len(input_shape) != 3:
        raise ValueError('Inputs should have rank 3. Received input shape: ' +
                         str(input_shape))
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs '
                         'should be defined. Found `None`.')
      input_dim = int(input_shape[channel_axis])
      self.input_spec = InputSpec(ndim=3, axes={channel_axis: input_dim})
      kernel_shape = self.kernel_size + (self.filters, input_dim)
  
      self.kernel = self.add_weight(
          name='kernel',
          shape=kernel_shape,
          initializer=self.kernel_initializer,
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint,
          trainable=True,
          dtype=self.dtype)
      if self.use_bias:
        self.bias = self.add_weight(
            name='bias',
            shape=(self.filters,),
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
            trainable=True,
            dtype=self.dtype)
      else:
        self.bias = None
      self.built = True
  
    def call(self, inputs):
      inputs_shape = array_ops.shape(inputs)
      batch_size = inputs_shape[0]
      if self.data_format == 'channels_first':
        t_axis = 2
      else:
        t_axis = 1
  
      length = inputs_shape[t_axis]
      if self.output_padding is None:
        output_padding = None
      else:
        output_padding = self.output_padding[0]
  
      # Infer the dynamic output shape:
      out_length = conv_utils.deconv_output_length(
          length, self.kernel_size[0], padding=self.padding,
          output_padding=output_padding, stride=self.strides[0],
          dilation=self.dilation_rate[0])
      if self.data_format == 'channels_first':
        output_shape = (batch_size, self.filters, out_length)
      else:
        output_shape = (batch_size, out_length, self.filters)
      data_format = conv_utils.convert_data_format(self.data_format, ndim=3)
  
      output_shape_tensor = array_ops.stack(output_shape)
      outputs = nn_ops.conv1d_transpose(
          inputs,
          self.kernel,
          output_shape_tensor,
          strides=self.strides,
          padding=self.padding.upper(),
          data_format=data_format,
          dilations=self.dilation_rate)
  
      if not context.executing_eagerly():
        # Infer the static output shape:
        out_shape = self.compute_output_shape(inputs.shape)
        outputs.set_shape(out_shape)
  
      if self.use_bias:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=data_format)
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      output_shape = list(input_shape)
      if self.data_format == 'channels_first':
        c_axis, t_axis = 1, 2
      else:
        c_axis, t_axis = 2, 1
  
      if self.output_padding is None:
        output_padding = None
      else:
        output_padding = self.output_padding[0]
      output_shape[c_axis] = self.filters
      output_shape[t_axis] = conv_utils.deconv_output_length(
          output_shape[t_axis],
          self.kernel_size[0],
          padding=self.padding,
          output_padding=output_padding,
          stride=self.strides[0],
          dilation=self.dilation_rate[0])
      return tensor_shape.TensorShape(output_shape)
  
    def get_config(self):
      config = super(Conv1DTranspose, self).get_config()
      config['output_padding'] = self.output_padding
      return config
  
  
  @keras_export('keras.layers.Conv2DTranspose',
                'keras.layers.Convolution2DTranspose')
  class Conv2DTranspose(Conv2D):
    """Transposed convolution layer (sometimes called Deconvolution).
  
    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
    in `data_format="channels_last"`.
  
    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of 2 integers, specifying the
        height and width of the 2D convolution window.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      strides: An integer or tuple/list of 2 integers,
        specifying the strides of the convolution along the height and width.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      output_padding: An integer or tuple/list of 2 integers,
        specifying the amount of padding along the height and width
        of the output tensor.
        Can be a single integer to specify the same value for all
        spatial dimensions.
        The amount of output padding along a given dimension must be
        lower than the stride along that same dimension.
        If set to `None` (default), the output shape is inferred.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
      dilation_rate: an integer or tuple/list of 2 integers, specifying
        the dilation rate to use for dilated convolution.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (
        see `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (
        see `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation") (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      4D tensor with shape:
      `(batch_size, channels, rows, cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(batch_size, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(batch_size, filters, new_rows, new_cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(batch_size, new_rows, new_cols, filters)` if data_format='channels_last'.
      `rows` and `cols` values might have changed due to padding.
      If `output_padding` is specified:
      ```
      new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
      output_padding[0])
      new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
      output_padding[1])
      ```
  
    Returns:
      A tensor of rank 4 representing
      `activation(conv2dtranspose(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
  
    References:
      - [A guide to convolution arithmetic for deep
        learning](https://arxiv.org/abs/1603.07285v1)
      - [Deconvolutional
        Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 output_padding=None,
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv2DTranspose, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
      self.output_padding = output_padding
      if self.output_padding is not None:
        self.output_padding = conv_utils.normalize_tuple(
            self.output_padding, 2, 'output_padding')
        for stride, out_pad in zip(self.strides, self.output_padding):
          if out_pad >= stride:
            raise ValueError('Stride ' + str(self.strides) + ' must be '
                             'greater than output padding ' +
                             str(self.output_padding))
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape)
      if len(input_shape) != 4:
        raise ValueError('Inputs should have rank 4. Received input '
                         'shape: ' + str(input_shape))
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs '
                         'should be defined. Found `None`.')
      input_dim = int(input_shape[channel_axis])
      self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
      kernel_shape = self.kernel_size + (self.filters, input_dim)
  
      self.kernel = self.add_weight(
          name='kernel',
          shape=kernel_shape,
          initializer=self.kernel_initializer,
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint,
          trainable=True,
          dtype=self.dtype)
      if self.use_bias:
        self.bias = self.add_weight(
            name='bias',
            shape=(self.filters,),
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
            trainable=True,
            dtype=self.dtype)
      else:
        self.bias = None
      self.built = True
  
    def call(self, inputs):
      inputs_shape = array_ops.shape(inputs)
      batch_size = inputs_shape[0]
      if self.data_format == 'channels_first':
        h_axis, w_axis = 2, 3
      else:
        h_axis, w_axis = 1, 2
  
      # Use the constant height and weight when possible.
      # TODO(scottzhu): Extract this into a utility function that can be applied
      # to all convolutional layers, which currently lost the static shape
      # information due to tf.shape().
      height, width = None, None
      if inputs.shape.rank is not None:
        dims = inputs.shape.as_list()
        height = dims[h_axis]
        width = dims[w_axis]
      height = height if height is not None else inputs_shape[h_axis]
      width = width if width is not None else inputs_shape[w_axis]
  
      kernel_h, kernel_w = self.kernel_size
      stride_h, stride_w = self.strides
  
      if self.output_padding is None:
        out_pad_h = out_pad_w = None
      else:
        out_pad_h, out_pad_w = self.output_padding
  
      # Infer the dynamic output shape:
      out_height = conv_utils.deconv_output_length(height,
                                                   kernel_h,
                                                   padding=self.padding,
                                                   output_padding=out_pad_h,
                                                   stride=stride_h,
                                                   dilation=self.dilation_rate[0])
      out_width = conv_utils.deconv_output_length(width,
                                                  kernel_w,
                                                  padding=self.padding,
                                                  output_padding=out_pad_w,
                                                  stride=stride_w,
                                                  dilation=self.dilation_rate[1])
      if self.data_format == 'channels_first':
        output_shape = (batch_size, self.filters, out_height, out_width)
      else:
        output_shape = (batch_size, out_height, out_width, self.filters)
  
      output_shape_tensor = array_ops.stack(output_shape)
      outputs = backend.conv2d_transpose(
          inputs,
          self.kernel,
          output_shape_tensor,
          strides=self.strides,
          padding=self.padding,
          data_format=self.data_format,
          dilation_rate=self.dilation_rate)
  
      if not context.executing_eagerly():
        # Infer the static output shape:
        out_shape = self.compute_output_shape(inputs.shape)
        outputs.set_shape(out_shape)
  
      if self.use_bias:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      output_shape = list(input_shape)
      if self.data_format == 'channels_first':
        c_axis, h_axis, w_axis = 1, 2, 3
      else:
        c_axis, h_axis, w_axis = 3, 1, 2
  
      kernel_h, kernel_w = self.kernel_size
      stride_h, stride_w = self.strides
  
      if self.output_padding is None:
        out_pad_h = out_pad_w = None
      else:
        out_pad_h, out_pad_w = self.output_padding
  
      output_shape[c_axis] = self.filters
      output_shape[h_axis] = conv_utils.deconv_output_length(
          output_shape[h_axis],
          kernel_h,
          padding=self.padding,
          output_padding=out_pad_h,
          stride=stride_h,
          dilation=self.dilation_rate[0])
      output_shape[w_axis] = conv_utils.deconv_output_length(
          output_shape[w_axis],
          kernel_w,
          padding=self.padding,
          output_padding=out_pad_w,
          stride=stride_w,
          dilation=self.dilation_rate[1])
      return tensor_shape.TensorShape(output_shape)
  
    def get_config(self):
      config = super(Conv2DTranspose, self).get_config()
      config['output_padding'] = self.output_padding
      return config
  
  
  @keras_export('keras.layers.Conv3DTranspose',
                'keras.layers.Convolution3DTranspose')
  class Conv3DTranspose(Conv3D):
    """Transposed convolution layer (sometimes called Deconvolution).
  
    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
    if `data_format="channels_last"`.
  
    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of 3 integers, specifying the
        depth, height and width of the 3D convolution window.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      strides: An integer or tuple/list of 3 integers,
        specifying the strides of the convolution along the depth, height
          and width.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      output_padding: An integer or tuple/list of 3 integers,
        specifying the amount of padding along the depth, height, and
        width.
        Can be a single integer to specify the same value for all
        spatial dimensions.
        The amount of output padding along a given dimension must be
        lower than the stride along that same dimension.
        If set to `None` (default), the output shape is inferred.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, depth, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, depth, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
      dilation_rate: an integer or tuple/list of 3 integers, specifying
        the dilation rate to use for dilated convolution.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (
        see `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (
        see `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix (
        see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation") (
        see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      5D tensor with shape:
      `(batch_size, channels, depth, rows, cols)` if data_format='channels_first'
      or 5D tensor with shape:
      `(batch_size, depth, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
      5D tensor with shape:
      `(batch_size, filters, new_depth, new_rows, new_cols)` if
        data_format='channels_first'
      or 5D tensor with shape:
      `(batch_size, new_depth, new_rows, new_cols, filters)` if
        data_format='channels_last'.
      `depth` and `rows` and `cols` values might have changed due to padding.
      If `output_padding` is specified::
      ```
      new_depth = ((depth - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
      output_padding[0])
      new_rows = ((rows - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
      output_padding[1])
      new_cols = ((cols - 1) * strides[2] + kernel_size[2] - 2 * padding[2] +
      output_padding[2])
      ```
  
    Returns:
      A tensor of rank 5 representing
      `activation(conv3dtranspose(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
  
    References:
      - [A guide to convolution arithmetic for deep
        learning](https://arxiv.org/abs/1603.07285v1)
      - [Deconvolutional
        Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 output_padding=None,
                 data_format=None,
                 dilation_rate=(1, 1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv3DTranspose, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
      self.output_padding = output_padding
      if self.output_padding is not None:
        self.output_padding = conv_utils.normalize_tuple(
            self.output_padding, 3, 'output_padding')
        for stride, out_pad in zip(self.strides, self.output_padding):
          if out_pad >= stride:
            raise ValueError('Stride ' + str(self.strides) + ' must be '
                             'greater than output padding ' +
                             str(self.output_padding))
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape)
      if len(input_shape) != 5:
        raise ValueError('Inputs should have rank 5, received input shape:',
                         str(input_shape))
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs '
                         'should be defined, found None: ' + str(input_shape))
      input_dim = int(input_shape[channel_axis])
      kernel_shape = self.kernel_size + (self.filters, input_dim)
      self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
  
      self.kernel = self.add_weight(
          'kernel',
          shape=kernel_shape,
          initializer=self.kernel_initializer,
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint,
          trainable=True,
          dtype=self.dtype)
      if self.use_bias:
        self.bias = self.add_weight(
            'bias',
            shape=(self.filters,),
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
            trainable=True,
            dtype=self.dtype)
      else:
        self.bias = None
      self.built = True
  
    def call(self, inputs):
      inputs_shape = array_ops.shape(inputs)
      batch_size = inputs_shape[0]
      if self.data_format == 'channels_first':
        d_axis, h_axis, w_axis = 2, 3, 4
      else:
        d_axis, h_axis, w_axis = 1, 2, 3
  
      depth = inputs_shape[d_axis]
      height = inputs_shape[h_axis]
      width = inputs_shape[w_axis]
  
      kernel_d, kernel_h, kernel_w = self.kernel_size
      stride_d, stride_h, stride_w = self.strides
  
      if self.output_padding is None:
        out_pad_d = out_pad_h = out_pad_w = None
      else:
        out_pad_d, out_pad_h, out_pad_w = self.output_padding
  
      # Infer the dynamic output shape:
      out_depth = conv_utils.deconv_output_length(depth,
                                                  kernel_d,
                                                  padding=self.padding,
                                                  output_padding=out_pad_d,
                                                  stride=stride_d)
      out_height = conv_utils.deconv_output_length(height,
                                                   kernel_h,
                                                   padding=self.padding,
                                                   output_padding=out_pad_h,
                                                   stride=stride_h)
      out_width = conv_utils.deconv_output_length(width,
                                                  kernel_w,
                                                  padding=self.padding,
                                                  output_padding=out_pad_w,
                                                  stride=stride_w)
      if self.data_format == 'channels_first':
        output_shape = (batch_size, self.filters, out_depth, out_height,
                        out_width)
        strides = (1, 1, stride_d, stride_h, stride_w)
      else:
        output_shape = (batch_size, out_depth, out_height, out_width,
                        self.filters)
        strides = (1, stride_d, stride_h, stride_w, 1)
  
      output_shape_tensor = array_ops.stack(output_shape)
      outputs = nn.conv3d_transpose(
          inputs,
          self.kernel,
          output_shape_tensor,
          strides,
          data_format=conv_utils.convert_data_format(self.data_format, ndim=5),
          padding=self.padding.upper())
  
      if not context.executing_eagerly():
        # Infer the static output shape:
        out_shape = self.compute_output_shape(inputs.shape)
        outputs.set_shape(out_shape)
  
      if self.use_bias:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      output_shape = list(input_shape)
      if self.data_format == 'channels_first':
        c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
      else:
        c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
  
      kernel_d, kernel_h, kernel_w = self.kernel_size
      stride_d, stride_h, stride_w = self.strides
  
      if self.output_padding is None:
        out_pad_d = out_pad_h = out_pad_w = None
      else:
        out_pad_d, out_pad_h, out_pad_w = self.output_padding
  
      output_shape[c_axis] = self.filters
      output_shape[d_axis] = conv_utils.deconv_output_length(
          output_shape[d_axis],
          kernel_d,
          padding=self.padding,
          output_padding=out_pad_d,
          stride=stride_d)
      output_shape[h_axis] = conv_utils.deconv_output_length(
          output_shape[h_axis],
          kernel_h,
          padding=self.padding,
          output_padding=out_pad_h,
          stride=stride_h)
      output_shape[w_axis] = conv_utils.deconv_output_length(
          output_shape[w_axis],
          kernel_w,
          padding=self.padding,
          output_padding=out_pad_w,
          stride=stride_w)
      return tensor_shape.TensorShape(output_shape)
  
    def get_config(self):
      config = super(Conv3DTranspose, self).get_config()
      config.pop('dilation_rate')
      config['output_padding'] = self.output_padding
      return config
  
  
  class SeparableConv(Conv):
    """Abstract base layer for separable nD convolution.
  
    This layer performs a depthwise convolution that acts separately on
    channels, followed by a pointwise convolution that mixes channels.
    If `use_bias` is True and a bias initializer is provided,
    it adds a bias vector to the output.
    It then optionally applies an activation function to produce the final output.
  
    Args:
      rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: A tuple or list of integers specifying the spatial
        dimensions of the filters. Can be a single integer to specify the same
        value for all spatial dimensions.
      strides: A tuple or list of integers specifying the strides
        of the convolution. Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any `stride` value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, ..., channels)` while `channels_first` corresponds to
        inputs with shape `(batch_size, channels, ...)`.
      dilation_rate: An integer or tuple/list of 2 integers, specifying
        the dilation rate to use for dilated convolution.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      depth_multiplier: The number of depthwise convolution output channels for
        each input channel. The total number of depthwise convolution output
        channels will be equal to `num_filters_in * depth_multiplier`.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias.
      depthwise_initializer: An initializer for the depthwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer (
        'glorot_uniform') will be used.
      pointwise_initializer: An initializer for the pointwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer 
        ('glorot_uniform') will be used.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer ('zeros') will be used (see `keras.initializers`).
      depthwise_regularizer: Optional regularizer for the depthwise
        convolution kernel.
      pointwise_regularizer: Optional regularizer for the pointwise
        convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      depthwise_constraint: Optional projection function to be applied to the
        depthwise kernel after being updated by an `Optimizer` (e.g. used for
        norm constraints or value constraints for layer weights). The function
        must take as input the unprojected variable and must return the
        projected variable (which must have the same shape). Constraints are
        not safe to use when doing asynchronous distributed training.
      pointwise_constraint: Optional projection function to be applied to the
        pointwise kernel after being updated by an `Optimizer`.
      bias_constraint: Optional projection function to be applied to the
        bias after being updated by an `Optimizer`.
      trainable: Boolean, if `True` the weights of this layer will be marked as
        trainable (and listed in `layer.trainable_weights`).
    """
  
    def __init__(self,
                 rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 depth_multiplier=1,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 pointwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 pointwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 pointwise_constraint=None,
                 bias_constraint=None,
                 trainable=True,
                 name=None,
                 **kwargs):
      super(SeparableConv, self).__init__(
          rank=rank,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          bias_initializer=initializers.get(bias_initializer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          bias_constraint=bias_constraint,
          trainable=trainable,
          name=name,
          **kwargs)
      self.depth_multiplier = depth_multiplier
      self.depthwise_initializer = initializers.get(depthwise_initializer)
      self.pointwise_initializer = initializers.get(pointwise_initializer)
      self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
      self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
      self.depthwise_constraint = constraints.get(depthwise_constraint)
      self.pointwise_constraint = constraints.get(pointwise_constraint)
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape)
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs '
                         'should be defined. Found `None`.')
      input_dim = int(input_shape[channel_axis])
      self.input_spec = InputSpec(ndim=self.rank + 2,
                                  axes={channel_axis: input_dim})
      depthwise_kernel_shape = self.kernel_size + (input_dim,
                                                   self.depth_multiplier)
      pointwise_kernel_shape = (
          1,) * self.rank + (self.depth_multiplier * input_dim, self.filters)
  
      self.depthwise_kernel = self.add_weight(
          name='depthwise_kernel',
          shape=depthwise_kernel_shape,
          initializer=self.depthwise_initializer,
          regularizer=self.depthwise_regularizer,
          constraint=self.depthwise_constraint,
          trainable=True,
          dtype=self.dtype)
      self.pointwise_kernel = self.add_weight(
          name='pointwise_kernel',
          shape=pointwise_kernel_shape,
          initializer=self.pointwise_initializer,
          regularizer=self.pointwise_regularizer,
          constraint=self.pointwise_constraint,
          trainable=True,
          dtype=self.dtype)
      if self.use_bias:
        self.bias = self.add_weight(
            name='bias',
            shape=(self.filters,),
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
            trainable=True,
            dtype=self.dtype)
      else:
        self.bias = None
      self.built = True
  
    def call(self, inputs):
      raise NotImplementedError
  
    def get_config(self):
      config = {
          'filters':
              self.filters,
          'kernel_size':
              self.kernel_size,
          'strides':
              self.strides,
          'padding':
              self.padding,
          'data_format':
              self.data_format,
          'depth_multiplier':
              self.depth_multiplier,
          'dilation_rate':
              self.dilation_rate,
          'activation':
              activations.serialize(self.activation),
          'use_bias':
              self.use_bias,
          'depthwise_initializer':
              initializers.serialize(self.depthwise_initializer),
          'pointwise_initializer':
              initializers.serialize(self.pointwise_initializer),
          'bias_initializer':
              initializers.serialize(self.bias_initializer),
          'depthwise_regularizer':
              regularizers.serialize(self.depthwise_regularizer),
          'pointwise_regularizer':
              regularizers.serialize(self.pointwise_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'depthwise_constraint':
              constraints.serialize(self.depthwise_constraint),
          'pointwise_constraint':
              constraints.serialize(self.pointwise_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(SeparableConv, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.SeparableConv1D',
                'keras.layers.SeparableConvolution1D')
  class SeparableConv1D(SeparableConv):
    """Depthwise separable 1D convolution.
  
    This layer performs a depthwise convolution that acts separately on
    channels, followed by a pointwise convolution that mixes channels.
    If `use_bias` is True and a bias initializer is provided,
    it adds a bias vector to the output.
    It then optionally applies an activation function to produce the final output.
  
    Args:
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: A single integer specifying the spatial
        dimensions of the filters.
      strides: A single integer specifying the strides
        of the convolution.
        Specifying any `stride` value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"`, `"same"`, or `"causal"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input. `"causal"` results in causal
        (dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`.
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, length, channels)` while `channels_first` corresponds to
        inputs with shape `(batch_size, channels, length)`.
      dilation_rate: A single integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      depth_multiplier: The number of depthwise convolution output channels for
        each input channel. The total number of depthwise convolution output
        channels will be equal to `num_filters_in * depth_multiplier`.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias.
      depthwise_initializer: An initializer for the depthwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer (
        'glorot_uniform') will be used.
      pointwise_initializer: An initializer for the pointwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer 
        ('glorot_uniform') will be used.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer ('zeros') will be used (see `keras.initializers`).
      depthwise_regularizer: Optional regularizer for the depthwise
        convolution kernel (see `keras.regularizers`).
      pointwise_regularizer: Optional regularizer for the pointwise
        convolution kernel (see `keras.regularizers`).
      bias_regularizer: Optional regularizer for the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Optional regularizer function for the output (
        see `keras.regularizers`).
      depthwise_constraint: Optional projection function to be applied to the
        depthwise kernel after being updated by an `Optimizer` (e.g. used for
        norm constraints or value constraints for layer weights). The function
        must take as input the unprojected variable and must return the
        projected variable (which must have the same shape). Constraints are
        not safe to use when doing asynchronous distributed training (
        see `keras.constraints`).
      pointwise_constraint: Optional projection function to be applied to the
        pointwise kernel after being updated by an `Optimizer` (
        see `keras.constraints`).
      bias_constraint: Optional projection function to be applied to the
        bias after being updated by an `Optimizer` (
        see `keras.constraints`).
      trainable: Boolean, if `True` the weights of this layer will be marked as
        trainable (and listed in `layer.trainable_weights`).
  
    Input shape:
      3D tensor with shape:
      `(batch_size, channels, steps)` if data_format='channels_first'
      or 5D tensor with shape:
      `(batch_size, steps, channels)` if data_format='channels_last'.
  
    Output shape:
      3D tensor with shape:
      `(batch_size, filters, new_steps)` if data_format='channels_first'
      or 3D tensor with shape:
      `(batch_size,  new_steps, filters)` if data_format='channels_last'.
      `new_steps` value might have changed due to padding or strides.
  
    Returns:
      A tensor of rank 3 representing
      `activation(separableconv1d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 depth_multiplier=1,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 pointwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 pointwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 pointwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(SeparableConv1D, self).__init__(
          rank=1,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          depth_multiplier=depth_multiplier,
          activation=activations.get(activation),
          use_bias=use_bias,
          depthwise_initializer=initializers.get(depthwise_initializer),
          pointwise_initializer=initializers.get(pointwise_initializer),
          bias_initializer=initializers.get(bias_initializer),
          depthwise_regularizer=regularizers.get(depthwise_regularizer),
          pointwise_regularizer=regularizers.get(pointwise_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          depthwise_constraint=constraints.get(depthwise_constraint),
          pointwise_constraint=constraints.get(pointwise_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def call(self, inputs):
      if self.padding == 'causal':
        inputs = array_ops.pad(inputs, self._compute_causal_padding(inputs))
      if self.data_format == 'channels_last':
        strides = (1,) + self.strides * 2 + (1,)
        spatial_start_dim = 1
      else:
        strides = (1, 1) + self.strides * 2
        spatial_start_dim = 2
  
      # Explicitly broadcast inputs and kernels to 4D.
      # TODO(fchollet): refactor when a native separable_conv1d op is available.
      inputs = array_ops.expand_dims(inputs, spatial_start_dim)
      depthwise_kernel = array_ops.expand_dims(self.depthwise_kernel, 0)
      pointwise_kernel = array_ops.expand_dims(self.pointwise_kernel, 0)
      dilation_rate = (1,) + self.dilation_rate
  
      if self.padding == 'causal':
        op_padding = 'valid'
      else:
        op_padding = self.padding
      outputs = nn.separable_conv2d(
          inputs,
          depthwise_kernel,
          pointwise_kernel,
          strides=strides,
          padding=op_padding.upper(),
          rate=dilation_rate,
          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      if self.use_bias:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      outputs = array_ops.squeeze(outputs, [spatial_start_dim])
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
  
  @keras_export('keras.layers.SeparableConv2D',
                'keras.layers.SeparableConvolution2D')
  class SeparableConv2D(SeparableConv):
    """Depthwise separable 2D convolution.
  
    Separable convolutions consist of first performing
    a depthwise spatial convolution
    (which acts on each input channel separately)
    followed by a pointwise convolution which mixes the resulting
    output channels. The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.
  
    Intuitively, separable convolutions can be understood as
    a way to factorize a convolution kernel into two smaller kernels,
    or as an extreme version of an Inception block.
  
    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of 2 integers, specifying the
        height and width of the 2D convolution window.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      strides: An integer or tuple/list of 2 integers,
        specifying the strides of the convolution along the height and width.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
      dilation_rate: An integer or tuple/list of 2 integers, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      depth_multiplier: The number of depthwise convolution output channels
        for each input channel.
        The total number of depthwise convolution output
        channels will be equal to `filters_in * depth_multiplier`.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      depthwise_initializer: An initializer for the depthwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer (
        'glorot_uniform') will be used.
      pointwise_initializer: An initializer for the pointwise convolution kernel (
        see `keras.initializers`). If None, then the default initializer 
        ('glorot_uniform') will be used.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer ('zeros') will be used (see `keras.initializers`).
      depthwise_regularizer: Regularizer function applied to
        the depthwise kernel matrix (see `keras.regularizers`).
      pointwise_regularizer: Regularizer function applied to
        the pointwise kernel matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation") (
        see `keras.regularizers`).
      depthwise_constraint: Constraint function applied to
        the depthwise kernel matrix (
        see `keras.constraints`).
      pointwise_constraint: Constraint function applied to
        the pointwise kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      4D tensor with shape:
      `(batch_size, channels, rows, cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(batch_size, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(batch_size, filters, new_rows, new_cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(batch_size, new_rows, new_cols, filters)` if data_format='channels_last'.
      `rows` and `cols` values might have changed due to padding.
  
    Returns:
      A tensor of rank 4 representing
      `activation(separableconv2d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 depth_multiplier=1,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 pointwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 pointwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 pointwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(SeparableConv2D, self).__init__(
          rank=2,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          depth_multiplier=depth_multiplier,
          activation=activations.get(activation),
          use_bias=use_bias,
          depthwise_initializer=initializers.get(depthwise_initializer),
          pointwise_initializer=initializers.get(pointwise_initializer),
          bias_initializer=initializers.get(bias_initializer),
          depthwise_regularizer=regularizers.get(depthwise_regularizer),
          pointwise_regularizer=regularizers.get(pointwise_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          depthwise_constraint=constraints.get(depthwise_constraint),
          pointwise_constraint=constraints.get(pointwise_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def call(self, inputs):
      # Apply the actual ops.
      if self.data_format == 'channels_last':
        strides = (1,) + self.strides + (1,)
      else:
        strides = (1, 1) + self.strides
      outputs = nn.separable_conv2d(
          inputs,
          self.depthwise_kernel,
          self.pointwise_kernel,
          strides=strides,
          padding=self.padding.upper(),
          rate=self.dilation_rate,
          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      if self.use_bias:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  
      if self.activation is not None:
        return self.activation(outputs)
      return outputs
  
  
  @keras_export('keras.layers.DepthwiseConv2D')
  class DepthwiseConv2D(Conv2D):
    """Depthwise separable 2D convolution.
  
    Depthwise Separable convolutions consist of performing
    just the first step in a depthwise spatial convolution
    (which acts on each input channel separately).
    The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.
  
    Args:
      kernel_size: An integer or tuple/list of 2 integers, specifying the
        height and width of the 2D convolution window.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      strides: An integer or tuple/list of 2 integers,
        specifying the strides of the convolution along the height and width.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: one of `'valid'` or `'same'` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros evenly
        to the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      depth_multiplier: The number of depthwise convolution output channels
        for each input channel.
        The total number of depthwise convolution output
        channels will be equal to `filters_in * depth_multiplier`.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be 'channels_last'.
      dilation_rate: An integer or tuple/list of 2 integers, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied (
        see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      depthwise_initializer: Initializer for the depthwise kernel matrix (
        see `keras.initializers`). If None, the default initializer (
        'glorot_uniform') will be used.
      bias_initializer: Initializer for the bias vector (
        see `keras.initializers`). If None, the default initializer (
        'zeros') will bs used.
      depthwise_regularizer: Regularizer function applied to
        the depthwise kernel matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (
        see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its 'activation') (
        see `keras.regularizers`).
      depthwise_constraint: Constraint function applied to
        the depthwise kernel matrix (
        see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (
        see `keras.constraints`).
  
    Input shape:
      4D tensor with shape:
      `[batch_size, channels, rows, cols]` if data_format='channels_first'
      or 4D tensor with shape:
      `[batch_size, rows, cols, channels]` if data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `[batch_size, channels * depth_multiplier, new_rows, new_cols]` if
      data_format='channels_first' or 4D tensor with shape:
      `[batch_size, new_rows, new_cols, channels * depth_multiplier]` if
      data_format='channels_last'. `rows` and `cols` values might have
      changed due to padding.
  
    Returns:
      A tensor of rank 4 representing
      `activation(depthwiseconv2d(inputs, kernel) + bias)`.
  
    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.
    """
  
    def __init__(self,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 depth_multiplier=1,
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(DepthwiseConv2D, self).__init__(
          filters=None,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activation,
          use_bias=use_bias,
          bias_regularizer=bias_regularizer,
          activity_regularizer=activity_regularizer,
          bias_constraint=bias_constraint,
          **kwargs)
      self.depth_multiplier = depth_multiplier
      self.depthwise_initializer = initializers.get(depthwise_initializer)
      self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
      self.depthwise_constraint = constraints.get(depthwise_constraint)
      self.bias_initializer = initializers.get(bias_initializer)
  
    def build(self, input_shape):
      if len(input_shape) < 4:
        raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
                         'Received input shape:', str(input_shape))
      input_shape = tensor_shape.TensorShape(input_shape)
      channel_axis = self._get_channel_axis()
      if input_shape.dims[channel_axis].value is None:
        raise ValueError('The channel dimension of the inputs to '
                         '`DepthwiseConv2D` '
                         'should be defined. Found `None`.')
      input_dim = int(input_shape[channel_axis])
      depthwise_kernel_shape = (self.kernel_size[0],
                                self.kernel_size[1],
                                input_dim,
                                self.depth_multiplier)
  
      self.depthwise_kernel = self.add_weight(
          shape=depthwise_kernel_shape,
          initializer=self.depthwise_initializer,
          name='depthwise_kernel',
          regularizer=self.depthwise_regularizer,
          constraint=self.depthwise_constraint)
  
      if self.use_bias:
        self.bias = self.add_weight(shape=(input_dim * self.depth_multiplier,),
                                    initializer=self.bias_initializer,
                                    name='bias',
                                    regularizer=self.bias_regularizer,
                                    constraint=self.bias_constraint)
      else:
        self.bias = None
      # Set input spec.
      self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
      self.built = True
  
    def call(self, inputs):
      outputs = backend.depthwise_conv2d(
          inputs,
          self.depthwise_kernel,
          strides=self.strides,
          padding=self.padding,
          dilation_rate=self.dilation_rate,
          data_format=self.data_format)
  
      if self.use_bias:
        outputs = backend.bias_add(
            outputs,
            self.bias,
            data_format=self.data_format)
  
      if self.activation is not None:
        return self.activation(outputs)
  
      return outputs
  
    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
      if self.data_format == 'channels_first':
        rows = input_shape[2]
        cols = input_shape[3]
        out_filters = input_shape[1] * self.depth_multiplier
      elif self.data_format == 'channels_last':
        rows = input_shape[1]
        cols = input_shape[2]
        out_filters = input_shape[3] * self.depth_multiplier
  
      rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
                                           self.padding,
                                           self.strides[0],
                                           self.dilation_rate[0])
      cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
                                           self.padding,
                                           self.strides[1],
                                           self.dilation_rate[1])
      if self.data_format == 'channels_first':
        return (input_shape[0], out_filters, rows, cols)
      elif self.data_format == 'channels_last':
        return (input_shape[0], rows, cols, out_filters)
  
    def get_config(self):
      config = super(DepthwiseConv2D, self).get_config()
      config.pop('filters')
      config.pop('kernel_initializer')
      config.pop('kernel_regularizer')
      config.pop('kernel_constraint')
      config['depth_multiplier'] = self.depth_multiplier
      config['depthwise_initializer'] = initializers.serialize(
          self.depthwise_initializer)
      config['depthwise_regularizer'] = regularizers.serialize(
          self.depthwise_regularizer)
      config['depthwise_constraint'] = constraints.serialize(
          self.depthwise_constraint)
      return config
  
  
  @keras_export('keras.layers.UpSampling1D')
  class UpSampling1D(Layer):
    """Upsampling layer for 1D inputs.
  
    Repeats each temporal step `size` times along the time axis.
  
    Examples:
  
    >>> input_shape = (2, 2, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[ 0  1  2]
      [ 3  4  5]]
     [[ 6  7  8]
      [ 9 10 11]]]
    >>> y = tf.keras.layers.UpSampling1D(size=2)(x)
    >>> print(y)
    tf.Tensor(
      [[[ 0  1  2]
        [ 0  1  2]
        [ 3  4  5]
        [ 3  4  5]]
       [[ 6  7  8]
        [ 6  7  8]
        [ 9 10 11]
        [ 9 10 11]]], shape=(2, 4, 3), dtype=int64)
  
    Args:
      size: Integer. Upsampling factor.
  
    Input shape:
      3D tensor with shape: `(batch_size, steps, features)`.
  
    Output shape:
      3D tensor with shape: `(batch_size, upsampled_steps, features)`.
    """
  
    def __init__(self, size=2, **kwargs):
      super(UpSampling1D, self).__init__(**kwargs)
      self.size = int(size)
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      size = self.size * input_shape[1] if input_shape[1] is not None else None
      return tensor_shape.TensorShape([input_shape[0], size, input_shape[2]])
  
    def call(self, inputs):
      output = backend.repeat_elements(inputs, self.size, axis=1)
      return output
  
    def get_config(self):
      config = {'size': self.size}
      base_config = super(UpSampling1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.UpSampling2D')
  class UpSampling2D(Layer):
    """Upsampling layer for 2D inputs.
  
    Repeats the rows and columns of the data
    by `size[0]` and `size[1]` respectively.
  
    Examples:
  
    >>> input_shape = (2, 2, 1, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[[ 0  1  2]]
      [[ 3  4  5]]]
     [[[ 6  7  8]]
      [[ 9 10 11]]]]
    >>> y = tf.keras.layers.UpSampling2D(size=(1, 2))(x)
    >>> print(y)
    tf.Tensor(
      [[[[ 0  1  2]
         [ 0  1  2]]
        [[ 3  4  5]
         [ 3  4  5]]]
       [[[ 6  7  8]
         [ 6  7  8]]
        [[ 9 10 11]
         [ 9 10 11]]]], shape=(2, 2, 2, 3), dtype=int64)
  
    Args:
      size: Int, or tuple of 2 integers.
        The upsampling factors for rows and columns.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
      interpolation: A string, one of `nearest` or `bilinear`.
  
    Input shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, rows, cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, rows, cols)`
  
    Output shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, upsampled_rows, upsampled_cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, upsampled_rows, upsampled_cols)`
    """
  
    def __init__(self,
                 size=(2, 2),
                 data_format=None,
                 interpolation='nearest',
                 **kwargs):
      super(UpSampling2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.size = conv_utils.normalize_tuple(size, 2, 'size')
      if interpolation not in {'nearest', 'bilinear'}:
        raise ValueError('`interpolation` argument should be one of `"nearest"` '
                         'or `"bilinear"`.')
      self.interpolation = interpolation
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        height = self.size[0] * input_shape[
            2] if input_shape[2] is not None else None
        width = self.size[1] * input_shape[
            3] if input_shape[3] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], height, width])
      else:
        height = self.size[0] * input_shape[
            1] if input_shape[1] is not None else None
        width = self.size[1] * input_shape[
            2] if input_shape[2] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], height, width, input_shape[3]])
  
    def call(self, inputs):
      return backend.resize_images(
          inputs, self.size[0], self.size[1], self.data_format,
          interpolation=self.interpolation)
  
    def get_config(self):
      config = {
          'size': self.size,
          'data_format': self.data_format,
          'interpolation': self.interpolation
      }
      base_config = super(UpSampling2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.UpSampling3D')
  class UpSampling3D(Layer):
    """Upsampling layer for 3D inputs.
  
    Repeats the 1st, 2nd and 3rd dimensions
    of the data by `size[0]`, `size[1]` and `size[2]` respectively.
  
    Examples:
  
    >>> input_shape = (2, 1, 2, 1, 3)
    >>> x = tf.constant(1, shape=input_shape)
    >>> y = tf.keras.layers.UpSampling3D(size=2)(x)
    >>> print(y.shape)
    (2, 2, 4, 2, 3)
  
    Args:
      size: Int, or tuple of 3 integers.
        The upsampling factors for dim1, dim2 and dim3.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
        while `channels_first` corresponds to inputs with shape
        `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
  
    Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, dim1, dim2, dim3, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, dim1, dim2, dim3)`
  
    Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
    """
  
    def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.size = conv_utils.normalize_tuple(size, 3, 'size')
      self.input_spec = InputSpec(ndim=5)
      super(UpSampling3D, self).__init__(**kwargs)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        dim1 = self.size[0] * input_shape[
            2] if input_shape[2] is not None else None
        dim2 = self.size[1] * input_shape[
            3] if input_shape[3] is not None else None
        dim3 = self.size[2] * input_shape[
            4] if input_shape[4] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      else:
        dim1 = self.size[0] * input_shape[
            1] if input_shape[1] is not None else None
        dim2 = self.size[1] * input_shape[
            2] if input_shape[2] is not None else None
        dim3 = self.size[2] * input_shape[
            3] if input_shape[3] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
  
    def call(self, inputs):
      return backend.resize_volumes(
          inputs, self.size[0], self.size[1], self.size[2], self.data_format)
  
    def get_config(self):
      config = {'size': self.size, 'data_format': self.data_format}
      base_config = super(UpSampling3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.ZeroPadding1D')
  class ZeroPadding1D(Layer):
    """Zero-padding layer for 1D input (e.g. temporal sequence).
  
    Examples:
  
    >>> input_shape = (2, 2, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[ 0  1  2]
      [ 3  4  5]]
     [[ 6  7  8]
      [ 9 10 11]]]
    >>> y = tf.keras.layers.ZeroPadding1D(padding=2)(x)
    >>> print(y)
    tf.Tensor(
      [[[ 0  0  0]
        [ 0  0  0]
        [ 0  1  2]
        [ 3  4  5]
        [ 0  0  0]
        [ 0  0  0]]
       [[ 0  0  0]
        [ 0  0  0]
        [ 6  7  8]
        [ 9 10 11]
        [ 0  0  0]
        [ 0  0  0]]], shape=(2, 6, 3), dtype=int64)
  
    Args:
        padding: Int, or tuple of int (length 2), or dictionary.
            - If int:
            How many zeros to add at the beginning and end of
            the padding dimension (axis 1).
            - If tuple of int (length 2):
            How many zeros to add at the beginning and the end of
            the padding dimension (`(left_pad, right_pad)`).
  
    Input shape:
        3D tensor with shape `(batch_size, axis_to_pad, features)`
  
    Output shape:
        3D tensor with shape `(batch_size, padded_axis, features)`
    """
  
    def __init__(self, padding=1, **kwargs):
      super(ZeroPadding1D, self).__init__(**kwargs)
      self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      if input_shape[1] is not None:
        length = input_shape[1] + self.padding[0] + self.padding[1]
      else:
        length = None
      return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
  
    def call(self, inputs):
      return backend.temporal_padding(inputs, padding=self.padding)
  
    def get_config(self):
      config = {'padding': self.padding}
      base_config = super(ZeroPadding1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.ZeroPadding2D')
  class ZeroPadding2D(Layer):
    """Zero-padding layer for 2D input (e.g. picture).
  
    This layer can add rows and columns of zeros
    at the top, bottom, left and right side of an image tensor.
  
    Examples:
  
    >>> input_shape = (1, 1, 2, 2)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[[0 1]
       [2 3]]]]
    >>> y = tf.keras.layers.ZeroPadding2D(padding=1)(x)
    >>> print(y)
    tf.Tensor(
      [[[[0 0]
         [0 0]
         [0 0]
         [0 0]]
        [[0 0]
         [0 1]
         [2 3]
         [0 0]]
        [[0 0]
         [0 0]
         [0 0]
         [0 0]]]], shape=(1, 3, 4, 2), dtype=int64)
  
    Args:
      padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
        - If int: the same symmetric padding
          is applied to height and width.
        - If tuple of 2 ints:
          interpreted as two different
          symmetric padding values for height and width:
          `(symmetric_height_pad, symmetric_width_pad)`.
        - If tuple of 2 tuples of 2 ints:
          interpreted as
          `((top_pad, bottom_pad), (left_pad, right_pad))`
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
  
    Input shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, rows, cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, rows, cols)`
  
    Output shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, padded_rows, padded_cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, padded_rows, padded_cols)`
    """
  
    def __init__(self, padding=(1, 1), data_format=None, **kwargs):
      super(ZeroPadding2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(padding, int):
        self.padding = ((padding, padding), (padding, padding))
      elif hasattr(padding, '__len__'):
        if len(padding) != 2:
          raise ValueError('`padding` should have two elements. '
                           'Found: ' + str(padding))
        height_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                    '1st entry of padding')
        width_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                   '2nd entry of padding')
        self.padding = (height_padding, width_padding)
      else:
        raise ValueError('`padding` should be either an int, '
                         'a tuple of 2 ints '
                         '(symmetric_height_pad, symmetric_width_pad), '
                         'or a tuple of 2 tuples of 2 ints '
                         '((top_pad, bottom_pad), (left_pad, right_pad)). '
                         'Found: ' + str(padding))
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
        else:
          rows = None
        if input_shape[3] is not None:
          cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
        else:
          cols = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], rows, cols])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
        else:
          rows = None
        if input_shape[2] is not None:
          cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
        else:
          cols = None
        return tensor_shape.TensorShape(
            [input_shape[0], rows, cols, input_shape[3]])
  
    def call(self, inputs):
      return backend.spatial_2d_padding(
          inputs, padding=self.padding, data_format=self.data_format)
  
    def get_config(self):
      config = {'padding': self.padding, 'data_format': self.data_format}
      base_config = super(ZeroPadding2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.ZeroPadding3D')
  class ZeroPadding3D(Layer):
    """Zero-padding layer for 3D data (spatial or spatio-temporal).
  
    Examples:
  
    >>> input_shape = (1, 1, 2, 2, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> y = tf.keras.layers.ZeroPadding3D(padding=2)(x)
    >>> print(y.shape)
    (1, 5, 6, 6, 3)
  
    Args:
      padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
        - If int: the same symmetric padding
          is applied to height and width.
        - If tuple of 3 ints:
          interpreted as two different
          symmetric padding values for height and width:
          `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
        - If tuple of 3 tuples of 2 ints:
          interpreted as
          `((left_dim1_pad, right_dim1_pad), (left_dim2_pad,
            right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
        while `channels_first` corresponds to inputs with shape
        `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
  
    Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad,
            depth)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, depth, first_axis_to_pad, second_axis_to_pad,
            third_axis_to_pad)`
  
    Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad,
            depth)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, depth, first_padded_axis, second_padded_axis,
            third_axis_to_pad)`
    """
  
    def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
      super(ZeroPadding3D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(padding, int):
        self.padding = ((padding, padding), (padding, padding), (padding,
                                                                 padding))
      elif hasattr(padding, '__len__'):
        if len(padding) != 3:
          raise ValueError('`padding` should have 3 elements. '
                           'Found: ' + str(padding))
        dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                  '1st entry of padding')
        dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                  '2nd entry of padding')
        dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
                                                  '3rd entry of padding')
        self.padding = (dim1_padding, dim2_padding, dim3_padding)
      else:
        raise ValueError(
            '`padding` should be either an int, '
            'a tuple of 3 ints '
            '(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
            'or a tuple of 3 tuples of 2 ints '
            '((left_dim1_pad, right_dim1_pad),'
            ' (left_dim2_pad, right_dim2_pad),'
            ' (left_dim3_pad, right_dim2_pad)). '
            'Found: ' + str(padding))
      self.input_spec = InputSpec(ndim=5)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          dim1 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
        else:
          dim1 = None
        if input_shape[3] is not None:
          dim2 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
        else:
          dim2 = None
        if input_shape[4] is not None:
          dim3 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          dim1 = input_shape[1] + self.padding[0][0] + self.padding[0][1]
        else:
          dim1 = None
        if input_shape[2] is not None:
          dim2 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
        else:
          dim2 = None
        if input_shape[3] is not None:
          dim3 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
  
    def call(self, inputs):
      return backend.spatial_3d_padding(
          inputs, padding=self.padding, data_format=self.data_format)
  
    def get_config(self):
      config = {'padding': self.padding, 'data_format': self.data_format}
      base_config = super(ZeroPadding3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.Cropping1D')
  class Cropping1D(Layer):
    """Cropping layer for 1D input (e.g. temporal sequence).
  
    It crops along the time dimension (axis 1).
  
    Examples:
  
    >>> input_shape = (2, 3, 2)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[ 0  1]
      [ 2  3]
      [ 4  5]]
     [[ 6  7]
      [ 8  9]
      [10 11]]]
    >>> y = tf.keras.layers.Cropping1D(cropping=1)(x)
    >>> print(y)
    tf.Tensor(
      [[[2 3]]
       [[8 9]]], shape=(2, 1, 2), dtype=int64)
  
    Args:
      cropping: Int or tuple of int (length 2)
        How many units should be trimmed off at the beginning and end of
        the cropping dimension (axis 1).
        If a single int is provided, the same value will be used for both.
  
    Input shape:
      3D tensor with shape `(batch_size, axis_to_crop, features)`
  
    Output shape:
      3D tensor with shape `(batch_size, cropped_axis, features)`
    """
  
    def __init__(self, cropping=(1, 1), **kwargs):
      super(Cropping1D, self).__init__(**kwargs)
      self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if input_shape[1] is not None:
        length = input_shape[1] - self.cropping[0] - self.cropping[1]
      else:
        length = None
      return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
  
    def call(self, inputs):
      if self.cropping[1] == 0:
        return inputs[:, self.cropping[0]:, :]
      else:
        return inputs[:, self.cropping[0]:-self.cropping[1], :]
  
    def get_config(self):
      config = {'cropping': self.cropping}
      base_config = super(Cropping1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.Cropping2D')
  class Cropping2D(Layer):
    """Cropping layer for 2D input (e.g. picture).
  
    It crops along spatial dimensions, i.e. height and width.
  
    Examples:
  
    >>> input_shape = (2, 28, 28, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> y = tf.keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
    >>> print(y.shape)
    (2, 24, 20, 3)
  
    Args:
      cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
        - If int: the same symmetric cropping
          is applied to height and width.
        - If tuple of 2 ints:
          interpreted as two different
          symmetric cropping values for height and width:
          `(symmetric_height_crop, symmetric_width_crop)`.
        - If tuple of 2 tuples of 2 ints:
          interpreted as
          `((top_crop, bottom_crop), (left_crop, right_crop))`
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
  
    Input shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, rows, cols, channels)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, channels, rows, cols)`
  
    Output shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, cropped_rows, cropped_cols, channels)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, channels, cropped_rows, cropped_cols)`
    """
  
    def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs):
      super(Cropping2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(cropping, int):
        self.cropping = ((cropping, cropping), (cropping, cropping))
      elif hasattr(cropping, '__len__'):
        if len(cropping) != 2:
          raise ValueError('`cropping` should have two elements. '
                           'Found: ' + str(cropping))
        height_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                     '1st entry of cropping')
        width_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                    '2nd entry of cropping')
        self.cropping = (height_cropping, width_cropping)
      else:
        raise ValueError('`cropping` should be either an int, '
                         'a tuple of 2 ints '
                         '(symmetric_height_crop, symmetric_width_crop), '
                         'or a tuple of 2 tuples of 2 ints '
                         '((top_crop, bottom_crop), (left_crop, right_crop)). '
                         'Found: ' + str(cropping))
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        return tensor_shape.TensorShape([
            input_shape[0], input_shape[1],
            input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
            if input_shape[2] else None,
            input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
            if input_shape[3] else None
        ])
      else:
        return tensor_shape.TensorShape([
            input_shape[0],
            input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
            if input_shape[1] else None,
            input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
            if input_shape[2] else None, input_shape[3]
        ])
      # pylint: enable=invalid-unary-operand-type
  
    def call(self, inputs):
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:]
        elif self.cropping[0][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1]]
        elif self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:]
        return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                      self.cropping[1][0]:-self.cropping[1][1]]
      else:
        if self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:, :]
        elif self.cropping[0][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], :]
        elif self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, :]
        return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
            1][0]:-self.cropping[1][1], :]  # pylint: disable=invalid-unary-operand-type
      # pylint: enable=invalid-unary-operand-type
  
    def get_config(self):
      config = {'cropping': self.cropping, 'data_format': self.data_format}
      base_config = super(Cropping2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  @keras_export('keras.layers.Cropping3D')
  class Cropping3D(Layer):
    """Cropping layer for 3D data (e.g. spatial or spatio-temporal).
  
      Examples:
  
    >>> input_shape = (2, 28, 28, 10, 3)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> y = tf.keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
    >>> print(y.shape)
    (2, 24, 20, 6, 3)
  
    Args:
      cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
        - If int: the same symmetric cropping
          is applied to depth, height, and width.
        - If tuple of 3 ints: interpreted as two different
          symmetric cropping values for depth, height, and width:
          `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
        - If tuple of 3 tuples of 2 ints: interpreted as
          `((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
            right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
        while `channels_first` corresponds to inputs with shape
        `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".
  
    Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop,
          depth)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, depth, first_axis_to_crop, second_axis_to_crop,
          third_axis_to_crop)`
  
    Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis,
          depth)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, depth, first_cropped_axis, second_cropped_axis,
          third_cropped_axis)`
    """
  
    def __init__(self,
                 cropping=((1, 1), (1, 1), (1, 1)),
                 data_format=None,
                 **kwargs):
      super(Cropping3D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(cropping, int):
        self.cropping = ((cropping, cropping), (cropping, cropping), (cropping,
                                                                      cropping))
      elif hasattr(cropping, '__len__'):
        if len(cropping) != 3:
          raise ValueError('`cropping` should have 3 elements. '
                           'Found: ' + str(cropping))
        dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                   '1st entry of cropping')
        dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                   '2nd entry of cropping')
        dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
                                                   '3rd entry of cropping')
        self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
      else:
        raise ValueError(
            '`cropping` should be either an int, '
            'a tuple of 3 ints '
            '(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
            'or a tuple of 3 tuples of 2 ints '
            '((left_dim1_crop, right_dim1_crop),'
            ' (left_dim2_crop, right_dim2_crop),'
            ' (left_dim3_crop, right_dim2_crop)). '
            'Found: ' + str(cropping))
      self.input_spec = InputSpec(ndim=5)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
        else:
          dim1 = None
        if input_shape[3] is not None:
          dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
        else:
          dim2 = None
        if input_shape[4] is not None:
          dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          dim1 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
        else:
          dim1 = None
        if input_shape[2] is not None:
          dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
        else:
          dim2 = None
        if input_shape[3] is not None:
          dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
      # pylint: enable=invalid-unary-operand-type
  
    def call(self, inputs):
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:]
        elif self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, self.cropping[2][0]:]
        elif self.cropping[0][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], self.cropping[2][0]:]
        elif self.cropping[0][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][
              0]:-self.cropping[1][1], self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
                        cropping[1][0]:, self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
                        cropping[1][0]:-self.cropping[1][1], self.cropping[2][0]:]
        return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                      self.cropping[1][0]:-self.cropping[1][1], self.cropping[2][
                          0]:-self.cropping[2][1]]
      else:
        if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:, :]
        elif self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:-self.cropping[2][1], :]
        elif self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, self.cropping[2][0]:, :]
        elif self.cropping[0][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], self.cropping[2][0]:, :]
        elif self.cropping[0][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][
              0]:-self.cropping[1][1], self.cropping[2][0]:
                        -self.cropping[2][1], :]
        elif self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][
              0]:-self.cropping[0][1], self.cropping[1][0]:, self.cropping[2][0]:
                        -self.cropping[2][1], :]
        elif self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:-self.cropping[1][1], self.cropping[
                            2][0]:, :]
        return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
            1][0]:-self.cropping[1][1], self.cropping[2][0]:  # pylint: disable=invalid-unary-operand-type
                      -self.cropping[2][1], :]  # pylint: disable=invalid-unary-operand-type
      # pylint: enable=invalid-unary-operand-type
  
    def get_config(self):
      config = {'cropping': self.cropping, 'data_format': self.data_format}
      base_config = super(Cropping3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  # Aliases
  
  Convolution1D = Conv1D
  Convolution2D = Conv2D
  Convolution3D = Conv3D
  SeparableConvolution1D = SeparableConv1D
  SeparableConvolution2D = SeparableConv2D
  Convolution2DTranspose = Conv2DTranspose
  Convolution3DTranspose = Conv3DTranspose
  Deconvolution2D = Deconv2D = Conv2DTranspose
  Deconvolution3D = Deconv3D = Conv3DTranspose
  