class_docstr: An N-dimensional transpose convolutional module.
  
  Attributes:
     w: Weight variable. Note is `None` until module is connected.
     b: Biases variable. Note is `None` until module is connected.
     input_shape: The input shape of the first set of inputs. Note is `None`
       until module is connected.
class_docstr_tok: ["An","N-dimensional","transpose","convolutional","module",".","Attributes",":","w",":","Weight","variable",".","Note","is","`","None","`","until","module","is","connected",".","b",":","Biases","variable",".","Note","is","`","None","`","until","module","is","connected",".","input_shape",":","The","input","shape","of","the","first","set","of","inputs",".","Note","is","`","None","`","until","module","is","connected","."]
code: def _initialize(self, inputs):
      utils.assert_rank(inputs, self._num_spatial_dims + 2)
      self.input_channels = inputs.shape[self._channel_index]
      if self.input_channels is None:
          raise ValueError("The number of input channels must be known")
      self._dtype = inputs.dtype
  
      if self._output_shape is not None:
          if len(self._output_shape) != self._num_spatial_dims:
              raise ValueError(
                  "The output_shape must be of length {} but instead was {}.".format(
                      self._num_spatial_dims, len(self._output_shape)
                  )
              )
          if self._channel_index == 1:
              self._output_shape = [self._output_channels] + list(
                  self._output_shape
              )
          else:
              self._output_shape = list(self._output_shape) + [
                  self._output_channels
              ]
  
      self.w = self._make_w()
      if self._with_bias:
          self.b = tf.Variable(
              self._b_init((self._output_channels,), self._dtype),
              name="b",
          )
  
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 3 for calling the ConvNDTranspose obj's underlying member method code with example usage: obj._initialize(inputs) without return types