class_docstr: Depthwise separable 2D 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 tuple or list of 2 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 2 positive 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 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, height, width, channels)` while `channels_first` corresponds to
      inputs with shape `(batch, channels, height, width)`.
  
    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. Set it to None to maintain a
      linear activation.
    use_bias: Boolean, whether the layer uses a bias.
    depthwise_initializer: An initializer for the depthwise convolution kernel.
    pointwise_initializer: An initializer for the pointwise convolution kernel.
    bias_initializer: An initializer for the bias vector. If None, the default
      initializer will be used.
    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` also add variables to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
    name: A string, the name of the layer.
code: def __init__(
      self,
      filters,
      kernel_size,
      strides=(1, 1),
      padding="valid",
      data_format="channels_last",
      dilation_rate=(1, 1),
      depth_multiplier=1,
      activation=None,
      use_bias=True,
      depthwise_initializer=None,
      pointwise_initializer=None,
      bias_initializer=init_ops.zeros_initializer(),
      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(SeparableConv2D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          depth_multiplier=depth_multiplier,
          activation=activation,
          use_bias=use_bias,
          depthwise_initializer=depthwise_initializer,
          pointwise_initializer=pointwise_initializer,
          bias_initializer=bias_initializer,
          depthwise_regularizer=depthwise_regularizer,
          pointwise_regularizer=pointwise_regularizer,
          bias_regularizer=bias_regularizer,
          activity_regularizer=activity_regularizer,
          depthwise_constraint=depthwise_constraint,
          pointwise_constraint=pointwise_constraint,
          bias_constraint=bias_constraint,
          trainable=trainable,
          name=name,
          **kwargs,
      )
  
imports: import warnings
  from tensorflow.python.keras import layers
  from tensorflow.python.keras.legacy_tf_layers import base
  from tensorflow.python.ops import init_ops
label_desc: Use image node_id 1 to create a new SeparableConv2D object from inherited base classes: keras_layers, base with example: obj = SeparableConv2D(filters, kernel_size, strides, padding, data_format, dilation_rate, depth_multiplier, activation, use_bias, depthwise_initializer, pointwise_initializer, bias_initializer, depthwise_regularizer, pointwise_regularizer, bias_regularizer, activity_regularizer, depthwise_constraint, pointwise_constraint, bias_constraint, trainable, name)