code: def unpack_inputs(inputs):
      """unpack a tuple of `inputs` tensors to a tuple.
  
      Args:
        inputs: a list of tensors.
  
      Returns:
        a tuple of tensors. if any input is a special constant tensor, replace it
        with None.
      """
      inputs = tf.nest.flatten(inputs)
      outputs = []
      for x in inputs:
          if is_special_none_tensor(x):
              outputs.append(None)
          else:
              outputs.append(x)
      x = tuple(outputs)
  
      # To trick the very pointless 'unbalanced-tuple-unpacking' pylint check
      # from triggering.
      if len(x) == 1:
          return x[0]
      return tuple(outputs)
  
file_path: autodist/examples/benchmark/utils/tf_utils.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow
  from utils import activations
label_desc: Use image node_id 2 for calling a global function with example usage: unpack_inputs(inputs) and returns: tuple, x