class_docstr: Transposed 3D convolution layer (sometimes called 3D Deconvolution).
  
  Args:
    filters: Integer, the dimensionality of the output space (i.e. the number
      of filters in the convolution).
    kernel_size: An integer or tuple/list of 3 integers, specifying the
      depth, height and width of the 3D convolution window.
      Can be a single integer to specify the same value for all spatial
      dimensions.
    strides: An integer or tuple/list of 3 integers, specifying the strides
      of the convolution along the depth, height and width.
      Can be a single integer to specify the same value for all spatial
      dimensions.
    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, depth, height, width, channels)` while `channels_first`
      corresponds to inputs with shape
      `(batch, channels, depth, 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`, the default
      initializer will be used.
    kernel_regularizer: Optional regularizer for the convolution kernel.
    bias_regularizer: Optional regularizer for the bias vector.
    activity_regularizer: Optional regularizer function for the output.
    kernel_constraint: Optional projection function to be applied to the
        kernel after being updated by an `Optimizer` (e.g. used to implement
        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.
    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, 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,
      kernel_constraint=None,
      bias_constraint=None,
      trainable=True,
      name=None,
      **kwargs,
  ):
      super(Conv3DTranspose, self).__init__(
          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,
          kernel_constraint=kernel_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 Conv3DTranspose object from inherited base classes: keras_layers, base with example: obj = Conv3DTranspose(filters, kernel_size, strides, padding, data_format, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name)