code: def check_divisible_channels(input_tensor, n_channel_splits):
      """
      Check if the number of channels (last dim) of the input tensor
      is divisible by ``n_channel_splits``. If True, returns
      ``n_input_channels / n_channel_splits``, raises AssertionError otherwise
  
      :param input_tensor:
      :param n_channel_splits:
      :return: n_input_channels / n_channel_splits
      """
  
      n_input_channels = int(input_tensor.shape.as_list()[-1])
      n_channel_splits = int(n_channel_splits)
      assert (
          n_channel_splits > 0
          and n_input_channels % n_channel_splits == 0
      ), (
          "Number of feature channels should be divisible by "
          "n_channel_splits {}, so that given an input with n_input_channels, "
          "the output tensor will have "
          "n_input_channels / n_channel_splits.".format(
              n_channel_splits
          )
      )
      return n_input_channels / n_channel_splits
  
imports: from __future__ import absolute_import, print_function
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: check_divisible_channels(input_tensor, n_channel_splits) and returns: unknown