code: def complement_input_sample(model, input_sample):
      """
      This function will give a complemented input sample
      Mainly using default value to complete.
      """
      # forward_args = get_conditional_args(model, include="all", exclude=(bool, type(None)))
      forward_args = get_forward_args(model)
      forward_defaults = get_forward_defaults(model)
      input_sample_length = 1
      if isinstance(input_sample, Sequence):
          input_sample_length = len(input_sample)
  
      # check if need complement
      if len(forward_args) == input_sample_length:
          return input_sample
  
      # check if more input sample should be provided
      if (
          len(forward_args)
          > len(forward_defaults) + input_sample_length
      ):
          invalidInputError(False, "not enough input_sample provided!")
  
      # complement the input sample by defaults
      if isinstance(input_sample, Sequence):
          input_sample_complement = input_sample
          input_sample_complement += forward_defaults[
              -(len(forward_args) - input_sample_length) :
          ]
      else:
          input_sample_complement = []
          input_sample_complement.append(input_sample)
          input_sample_complement += list(
              forward_defaults[
                  -(len(forward_args) - input_sample_length) :
              ]
          )
  
      return tuple(input_sample_complement)
  
imports: from typing import Sequence
  from torch.utils.data import DataLoader
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.pytorch import get_forward_args, get_forward_defaults, get_conditional_args
label_desc: Use image node_id 1 for calling a global function with example usage: complement_input_sample(model, input_sample) and returns: tuple, input_sample