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 check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict):
      # make masks reproducible
      np.random.seed(2)
  
      num_patches = int(
          (tf_model.config.image_size // tf_model.config.patch_size)
          ** 2
      )
      noise = np.random.uniform(
          size=(self.model_tester.batch_size, num_patches)
      )
      tf_noise = tf.constant(noise)
  
      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      tf_inputs_dict["noise"] = tf_noise
  
      super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
  
imports: from __future__ import annotations
  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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 10 for calling the TFViTMAEModelTest obj's underlying member method code with example usage: obj.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) without return types