parameters: (inputs,
                       filters,
                       kernel_size,
                       strides=(1, 1),
                       padding='valid',
                       data_format='channels_last',
                       activation=None,
                       use_bias=True,
                       kernel_initializer=None,
                       bias_initializer=init_ops.zeros_initializer(),
                       kernel_regularizer=None,
                       bias_regularizer=None,
                       activity_regularizer=None,
                       trainable=True,
                       name=None,
                       reuse=None)
return_statement: return layer.apply(inputs)
docstring: Functional interface for transposed 2D convolution layer.
  
    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.
  
    Arguments:
      inputs: Input tensor.
      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 positive 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.
      padding: one of `"valid"` or `"same"` (case-insensitive).
      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)`.
      activation: Activation function. Set it to `None` to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: An initializer for the convolution kernel.
      bias_initializer: An initializer for the bias vector. If `None`, then no
        bias will be applied.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Regularizer function for the output.
      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.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
docstring_summary: Functional interface for transposed 2D convolution layer.
docstring_tokens: Functional interface for transposed 2D convolution layer .
function: def conv2d_transpose(inputs,
                       filters,
                       kernel_size,
                       strides=(1, 1),
                       padding='valid',
                       data_format='channels_last',
                       activation=None,
                       use_bias=True,
                       kernel_initializer=None,
                       bias_initializer=init_ops.zeros_initializer(),
                       kernel_regularizer=None,
                       bias_regularizer=None,
                       activity_regularizer=None,
                       trainable=True,
                       name=None,
                       reuse=None):
    """Functional interface for transposed 2D convolution layer.
  
    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.
  
    Arguments:
      inputs: Input tensor.
      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 positive 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.
      padding: one of `"valid"` or `"same"` (case-insensitive).
      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)`.
      activation: Activation function. Set it to `None` to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: An initializer for the convolution kernel.
      bias_initializer: An initializer for the bias vector. If `None`, then no
        bias will be applied.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Regularizer function for the output.
      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.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
    """
    layer = Conv2DTranspose(
        filters=filters,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        activation=activation,
        use_bias=use_bias,
        kernel_initializer=kernel_initializer,
        bias_initializer=bias_initializer,
        kernel_regularizer=kernel_regularizer,
        bias_regularizer=bias_regularizer,
        activity_regularizer=activity_regularizer,
        trainable=trainable,
        name=name,
        dtype=inputs.dtype.base_dtype,
        _reuse=reuse,
        _scope=name)
    return layer.apply(inputs)
function_tokens: def conv2d_transpose ( inputs , filters , kernel_size , strides = ( 1 , 1 ) , padding = 'valid' , data_format = 'channels_last' , activation = None , use_bias = True , kernel_initializer = None , bias_initializer = init_ops . zeros_initializer ( ) , kernel_regularizer = None , bias_regularizer = None , activity_regularizer = None , trainable = True , name = None , reuse = None ) : layer = Conv2DTranspose ( filters = filters , kernel_size = kernel_size , strides = strides , padding = padding , data_format = data_format , activation = activation , use_bias = use_bias , kernel_initializer = kernel_initializer , bias_initializer = bias_initializer , kernel_regularizer = kernel_regularizer , bias_regularizer = bias_regularizer , activity_regularizer = activity_regularizer , trainable = trainable , name = name , dtype = inputs . dtype . base_dtype , _reuse = reuse , _scope = name ) return layer . apply ( inputs )