class_docstr: A general N-dimensional convolutional module.
docstr: Constructs parameters used by this module.
docstr_tok: ["Constructs","parameters","used","by","this","module","."]
code: def _initialize(self, inputs: tf.Tensor):
      """Constructs parameters used by this module."""
      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
  
      self.w = self._make_w()
      if self.with_bias:
          self.b = tf.Variable(
              self.b_init((self.output_channels,), self._dtype),
              name="b",
          )
  
      if self.padding_func:
          self._padding = pad.create(
              padding=self.padding_func,
              kernel=self.kernel_shape,
              rate=self.rate,
              n=self._num_spatial_dims,
              channel_index=self._channel_index,
          )
  
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 3 for calling the ConvND obj's underlying member method code with example usage: obj._initialize(inputs) without return types