class_docstr: Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds,
  attention_mask and seq_length.
class_docstr_tok: ["Here","we","also","overwrite","some","of","the","tests","of","test_modeling_common.py",",","as","ViTMAE","does","not","use","input_ids",",","inputs_embeds",",","attention_mask","and","seq_length","."]
code: def test_keyword_and_dict_args(self):
      # make the mask reproducible
      np.random.seed(2)
  
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      num_patches = int((config.image_size // config.patch_size) ** 2)
      noise = np.random.uniform(
          size=(self.model_tester.batch_size, num_patches)
      )
  
      for model_class in self.all_model_classes:
          model = model_class(config)
          inputs = self._prepare_for_class(inputs_dict, model_class)
  
          outputs_dict = model(inputs, noise=noise)
  
          inputs_keywords = copy.deepcopy(
              self._prepare_for_class(inputs_dict, model_class)
          )
          outputs_keywords = model(**inputs_keywords, noise=noise)
          output_dict = outputs_dict[0].numpy()
          output_keywords = outputs_keywords[0].numpy()
  
          self.assertLess(
              np.sum(np.abs(output_dict - output_keywords)), 1e-6
          )
  
imports: import copy
  import inspect
  import json
  import math
  import os
  import tempfile
  import unittest
  from importlib import import_module
  import numpy
  from transformers import ViTMAEConfig
  from transformers.file_utils import cached_property, is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, require_vision, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 8 for calling the TFViTMAEModelTest obj's underlying member method code with example usage: obj.test_keyword_and_dict_args() without return types