code: def test_unpack_inputs(self):
      class DummyModel:
          def __init__(self):
              config_kwargs = {
                  "output_attentions": False,
                  "output_hidden_states": False,
                  "return_dict": False,
              }
              self.config = PretrainedConfig(**config_kwargs)
              self.main_input_name = "input_ids"
  
          @unpack_inputs
          def call(
              self,
              input_ids=None,
              past_key_values=None,
              output_attentions=None,
              output_hidden_states=None,
              return_dict=None,
          ):
              return (
                  input_ids,
                  past_key_values,
                  output_attentions,
                  output_hidden_states,
                  return_dict,
              )
  
          @unpack_inputs
          def foo(
              self,
              pixel_values,
              output_attentions=None,
              output_hidden_states=None,
              return_dict=None,
          ):
              return (
                  pixel_values,
                  output_attentions,
                  output_hidden_states,
                  return_dict,
              )
  
      dummy_model = DummyModel()
      input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int32)
      past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int32)
      pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int32)
  
      # test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config.
      output = dummy_model.call(
          input_ids=input_ids, past_key_values=past_key_values
      )
      tf.debugging.assert_equal(output[0], input_ids)
      tf.debugging.assert_equal(output[1], past_key_values)
      self.assertFalse(output[2])
      self.assertFalse(output[3])
      self.assertFalse(output[4])
  
      # test case 2: Same as above, but with positional arguments.
      output = dummy_model.call(input_ids, past_key_values)
      tf.debugging.assert_equal(output[0], input_ids)
      tf.debugging.assert_equal(output[1], past_key_values)
      self.assertFalse(output[2])
      self.assertFalse(output[3])
      self.assertFalse(output[4])
  
      # test case 3: We can also pack everything in the first input.
      output = dummy_model.call(
          input_ids={
              "input_ids": input_ids,
              "past_key_values": past_key_values,
          }
      )
      tf.debugging.assert_equal(output[0], input_ids)
      tf.debugging.assert_equal(output[1], past_key_values)
      self.assertFalse(output[2])
      self.assertFalse(output[3])
      self.assertFalse(output[4])
  
      # test case 4: Explicit boolean arguments should override the config.
      output = dummy_model.call(
          input_ids=input_ids,
          past_key_values=past_key_values,
          output_attentions=False,
          return_dict=True,
      )
      tf.debugging.assert_equal(output[0], input_ids)
      tf.debugging.assert_equal(output[1], past_key_values)
      self.assertFalse(output[2])
      self.assertFalse(output[3])
      self.assertTrue(output[4])
  
      # test case 5: Unexpected arguments should raise an exception.
      with self.assertRaises(ValueError):
          output = dummy_model.call(
              input_ids=input_ids,
              past_key_values=past_key_values,
              foo="bar",
          )
  
      # test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the
      # decorated function as its main input.
      output = dummy_model.foo(pixel_values=pixel_values)
      tf.debugging.assert_equal(output[0], pixel_values)
      self.assertFalse(output[1])
      self.assertFalse(output[2])
      self.assertFalse(output[3])
  
imports: from __future__ import annotations
  import inspect
  import json
  import os
  import random
  import tempfile
  import unittest
  import unittest.mock
  from huggingface_hub import HfFolder, Repository, delete_repo, snapshot_download
  from huggingface_hub.file_download import http_get
  from requests.exceptions import HTTPError
  from transformers import is_tf_available, is_torch_available
  from transformers.configuration_utils import PretrainedConfig
  from transformers.testing_utils import TOKEN, USER, CaptureLogger, _tf_gpu_memory_limit, is_pt_tf_cross_test, is_staging_test, require_safetensors, require_tf, require_torch, slow
  from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
label_desc: Use image node_id 4 for calling the TFModelUtilsTest obj's underlying member method code with example usage: obj.test_unpack_inputs() and returns: input_ids,  past_key_values,  output_attentions,  output_hidden_states,  return_dict, pixel_values,  output_attentions,  output_hidden_states,  return_dict