code: def test_safetensors_tf_from_sharded_h5_with_sharded_safetensors_local(
      self,
  ):
      with tempfile.TemporaryDirectory() as tmp_dir:
          path = snapshot_download(
              "hf-internal-testing/tiny-bert-tf-safetensors-h5-sharded",
              cache_dir=tmp_dir,
          )
  
          # This should not raise even if there are two types of sharded weights
          TFBertModel.from_pretrained(path)
  
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 21 for calling the TFModelUtilsTest obj's underlying member method code with example usage: obj.test_safetensors_tf_from_sharded_h5_with_sharded_safetensors_local() without return types