code: def create_and_check_for_masked_lm(
      self,
      config,
      input_ids,
      input_mask,
      token_type_ids,
      sequence_labels,
      token_labels,
      labels,
      numeric_values,
      numeric_values_scale,
      float_answer,
      aggregation_labels,
  ):
      model = TFTapasForMaskedLM(config=config)
      inputs = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
          "labels": token_labels,
      }
      result = model(inputs)
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.seq_length, self.vocab_size),
      )
  
imports: import copy
  import unittest
  import numpy
  import pandas
  from transformers import TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TapasConfig, TapasTokenizer, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tensorflow_probability, require_tf, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the TFTapasModelTester obj's underlying member method code with example usage: obj.create_and_check_for_masked_lm(config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels) without return types