code: def __init__(
      self,
      filters,
      kernel_size,
      strides=(1, 1, 1),
      padding="valid",
      data_format=None,
      dilation_rate=(1, 1, 1),
      activation=None,
      use_bias=True,
      kernel_initializer="glorot_uniform",
      bias_initializer="zeros",
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      **kwargs,
  ):
      super().__init__(
          rank=3,
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          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,
          **kwargs,
      )
  
imports: from keras.api_export import keras_export
  from keras.layers.convolutional.base_conv_transpose import BaseConvTranspose
label_desc: Use image node_id 1 to create a new Conv3DTranspose object from inherited base classes: BaseConvTranspose with example: obj = Conv3DTranspose(filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint)