code: def test_custom_load_tf_weights(self):
      (
          model,
          output_loading_info,
      ) = TFBertForTokenClassification.from_pretrained(
          "jplu/tiny-tf-bert-random", output_loading_info=True
      )
      self.assertEqual(
          sorted(output_loading_info["unexpected_keys"]), []
      )
      for layer in output_loading_info["missing_keys"]:
          self.assertTrue(
              layer.split("_")[0] in ["dropout", "classifier"]
          )
  
imports: import unittest
  from transformers import BertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
label_desc: Use image node_id 22 for calling the TFBertModelTest obj's underlying member method code with example usage: obj.test_custom_load_tf_weights() without return types