class_docstr: `Conv3D` module.
docstr: Constructs a ``Conv3D`` module.
  
  Args:
    output_channels: The number of output channels.
    kernel_shape: Sequence of kernel sizes (of length 3), or an integer.
      ``kernel_shape`` will be expanded to define a kernel size in all
      dimensions.
    stride: Sequence of strides (of length 3), or an integer. `stride` will be
      expanded to define stride in all dimensions.
    rate: Sequence of dilation rates (of length 3), or integer that is used to
      define dilation rate in all dimensions. 1 corresponds to standard
      convolution, ``rate > 1`` corresponds to dilated convolution.
    padding: Padding to apply to the input. This can either ``SAME``,
      ``VALID`` or a callable or sequence of callables up to size N. Any
      callables must take a single integer argument equal to the effective
      kernel size and return a list of two integers representing the padding
      before and after. See snt.pad.* for more details and example functions.
    with_bias: 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]],
      stride: Union[int, Sequence[int]] = 1,
      rate: Union[int, Sequence[int]] = 1,
      padding: Union[str, pad.Paddings] = "SAME",
      with_bias: bool = True,
      w_init: Optional[initializers.Initializer] = None,
      b_init: Optional[initializers.Initializer] = None,
      data_format: str = "NDHWC",
      name: Optional[str] = None,
  ):
      """Constructs a ``Conv3D`` module.
  
      Args:
        output_channels: The number of output channels.
        kernel_shape: Sequence of kernel sizes (of length 3), or an integer.
          ``kernel_shape`` will be expanded to define a kernel size in all
          dimensions.
        stride: Sequence of strides (of length 3), or an integer. `stride` will be
          expanded to define stride in all dimensions.
        rate: Sequence of dilation rates (of length 3), or integer that is used to
          define dilation rate in all dimensions. 1 corresponds to standard
          convolution, ``rate > 1`` corresponds to dilated convolution.
        padding: Padding to apply to the input. This can either ``SAME``,
          ``VALID`` or a callable or sequence of callables up to size N. Any
          callables must take a single integer argument equal to the effective
          kernel size and return a list of two integers representing the padding
          before and after. See snt.pad.* for more details and example functions.
        with_bias: 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=3,
          output_channels=output_channels,
          kernel_shape=kernel_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 pad
  from sonnet.src import utils
  import tensorflow
label_desc: Use image node_id 1 to create a new Conv3D object from inherited base classes: ConvND with example: obj = Conv3D(output_channels, kernel_shape, stride, rate, padding, with_bias, w_init, b_init, data_format, name)