code: def get_input_example(model, input_sample, forward_args):
      if isinstance(input_sample, DataLoader):
          input_sample = next(iter(input_sample))
          if isinstance(input_sample, Sequence):
              if len(input_sample) <= 2:
                  input_sample = input_sample[0]
              else:
                  input_sample = tuple(
                      input_sample[: len(forward_args)]
                  )
      elif input_sample is None:
          if getattr(model, "example_input_array", None) is not None:
              input_sample = model.example_input_array
          elif getattr(model, "trainer", None):
              # the default implementation of model.test_dataloader/train_dalaloader/val_dataloader
              # /predict_dataloader will throw an exception in pytorch lightning 1.6
              for dataloader_fn in [
                  model.test_dataloader,
                  model.train_dataloader,
                  model.val_dataloader,
              ]:
                  try:
                      dataloader = dataloader_fn()
                      input_sample = next(iter(dataloader))
                      if isinstance(input_sample, Sequence):
                          input_sample = tuple(
                              list(input_sample)[: len(forward_args)]
                          )
                      break
                  except Exception as _e:
                      pass
  
              if input_sample is None:
                  try:
                      predict_dataloader = model.predict_dataloader()
                      input_sample = tuple(
                          next(iter(predict_dataloader))
                      )
                  except Exception as _e:
                      pass
          else:
              invalidInputError(
                  False,
                  "You must specify an input_sample or call `Trainer.fit` "
                  "on the model first to use `eval_openvino`",
              )
  
      model.example_input_array = input_sample
      return input_sample
  
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 2 for calling a global function with example usage: get_input_example(model, input_sample, forward_args) and returns: input_sample