class_docstr: Transposed convolution operator for filtering windows of 2D inputs.
  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.
  Data format currently supported for this layer is dim_ordering='th' (Channel First).
  Border mode currently supported for this layer is 'valid'.
  You can also use Deconv2D as an alias of this layer.
  The input of this layer should be 4D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  e.g. input_shape=(3, 128, 128) for 128x128 RGB pictures.
  
  # Arguments
  nb_filter: Number of transposed convolution filters to use.
  nb_row: Number of rows in the convolution kernel.
  nb_col: Number of cols in the convolution kernel.
  output_shape: Output shape of the transposed convolution operation. Tuple of int.
  init: String representation of the initialization method for the weights of the layer.
        Default is 'glorot_uniform'.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Only 'valid' is supported for now.
  subsample: Int tuple of length 2 corresponding to the step of the convolution in the
             height and width dimension. Also called strides elsewhere. Default is (1, 1).
  dim_ordering: Format of input data. Only 'th' (Channel First) is supported for now.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  bias: Whether to include a bias (i.e. make the layer affine rather than linear).
        Default is True.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> deconv2d = Deconvolution2D(3, 3, 3, output_shape=(None, 3, 14, 14), input_shape=(3, 12, 12))
  creating: createZooKerasDeconvolution2D
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code: def __init__(
      self,
      nb_filter,
      nb_row,
      nb_col,
      output_shape,
      init="glorot_uniform",
      activation=None,
      border_mode="valid",
      subsample=(1, 1),
      dim_ordering="th",
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      if border_mode != "valid":
          raise ValueError(
              "For Deconvolution2D, only border_mode='valid' is supported for now"
          )
      super(Deconvolution2D, self).__init__(
          None,
          nb_filter,
          nb_row,
          nb_col,
          init,
          activation,
          subsample,
          dim_ordering,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new Deconvolution2D object from inherited base classes: ZooKerasLayer with example: obj = Deconvolution2D(nb_filter, nb_row, nb_col, output_shape, init, activation, border_mode, subsample, dim_ordering, W_regularizer, b_regularizer, bias, input_shape)