class_docstr: A 2D transpose convolutional module.
class_docstr_tok: ["A","2D","transpose","convolutional","module","."]
docstr: Constructs a `Conv2DTranspose` module.
  
  Args:
    output_channels: An integer, The number of output channels.
    kernel_shape: Sequence of integers (of length 2), or an integer
      representing kernel shape. `kernel_shape` will be expanded to define a
      kernel size in all dimensions.
    output_shape: Output shape of the spatial dimensions of a transpose
      convolution. Can be either an integer or an iterable of integers or
      `Dimension`s, or a `TensorShape` (of length 2). If a `None` value is
      given, a default shape is automatically calculated.
    stride: Sequence of integers (of length 2), or an integer. `stride` will
      be expanded to define stride in all dimensions.
    rate: Sequence of integers (of length 2), or integer that is used to
      define dilation rate in all dimensions. 1 corresponds to standard 2D
      convolution, `rate > 1` corresponds to dilated convolution.
    padding: Padding algorithm, either "SAME" or "VALID".
    with_bias: Boolean, whether to include bias parameters. Default `True`.
    w_init: Optional initializer for the weights. By default the weights are
      initialized truncated random normal values with a standard deviation of
      `1 / sqrt(input_feature_size)`, which is commonly used when the
      inputs are zero centered (see https://arxiv.org/abs/1502.03167v3).
    b_init: Optional initializer for the bias. By default the bias is
      initialized to zero.
    data_format: The data format of the input.
    name: Name of the module.
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code: def __init__(
      self,
      output_channels: int,
      kernel_shape: Union[int, Sequence[int]],
      output_shape: Optional[types.ShapeLike] = None,
      stride: Union[int, Sequence[int]] = 1,
      rate: Union[int, Sequence[int]] = 1,
      padding: str = "SAME",
      with_bias: bool = True,
      w_init: Optional[initializers.Initializer] = None,
      b_init: Optional[initializers.Initializer] = None,
      data_format: str = "NHWC",
      name: Optional[str] = None,
  ):
      """Constructs a `Conv2DTranspose` module.
  
      Args:
        output_channels: An integer, The number of output channels.
        kernel_shape: Sequence of integers (of length 2), or an integer
          representing kernel shape. `kernel_shape` will be expanded to define a
          kernel size in all dimensions.
        output_shape: Output shape of the spatial dimensions of a transpose
          convolution. Can be either an integer or an iterable of integers or
          `Dimension`s, or a `TensorShape` (of length 2). If a `None` value is
          given, a default shape is automatically calculated.
        stride: Sequence of integers (of length 2), or an integer. `stride` will
          be expanded to define stride in all dimensions.
        rate: Sequence of integers (of length 2), or integer that is used to
          define dilation rate in all dimensions. 1 corresponds to standard 2D
          convolution, `rate > 1` corresponds to dilated convolution.
        padding: Padding algorithm, either "SAME" or "VALID".
        with_bias: Boolean, whether to include bias parameters. Default `True`.
        w_init: Optional initializer for the weights. By default the weights are
          initialized truncated random normal values with a standard deviation of
          `1 / sqrt(input_feature_size)`, which is commonly used when the
          inputs are zero centered (see https://arxiv.org/abs/1502.03167v3).
        b_init: Optional initializer for the bias. By default the bias is
          initialized to zero.
        data_format: The data format of the input.
        name: Name of the module.
      """
      super().__init__(
          num_spatial_dims=2,
          output_channels=output_channels,
          kernel_shape=kernel_shape,
          output_shape=output_shape,
          stride=stride,
          rate=rate,
          padding=padding,
          with_bias=with_bias,
          w_init=w_init,
          b_init=b_init,
          data_format=data_format,
          name=name,
      )
  
imports: from typing import Optional, Sequence, Union
  import numpy
  from sonnet.src import base
  from sonnet.src import initializers
  from sonnet.src import once
  from sonnet.src import types
  from sonnet.src import utils
  import tensorflow
label_desc: Use image node_id 1 to create a new Conv2DTranspose object from inherited base classes: ConvNDTranspose with example: obj = Conv2DTranspose(output_channels, kernel_shape, output_shape, stride, rate, padding, with_bias, w_init, b_init, data_format, name)