code: def test_inference_classification_head(self):
      # note that google/tapas-base-finetuned-tabfact should correspond to tapas_tabfact_inter_masklm_base_reset
      model = TapasForSequenceClassification.from_pretrained(
          "google/tapas-base-finetuned-tabfact"
      ).to(torch_device)
  
      tokenizer = self.default_tokenizer
      table, queries = prepare_tapas_single_inputs_for_inference()
      inputs = tokenizer(
          table=table,
          queries=queries,
          padding="longest",
          return_tensors="pt",
      )
      inputs = {k: v.to(torch_device) for k, v in inputs.items()}
      with torch.no_grad():
          outputs = model(**inputs)
  
      # test the classification logits
      logits = outputs.logits
      expected_shape = torch.Size((1, 2))
      self.assertEqual(logits.shape, expected_shape)
      expected_tensor = torch.tensor(
          [[0.795137286, 9.5572]], device=torch_device
      )  # Note that the PyTorch model outputs [[0.8057, 9.5281]]
  
      self.assertTrue(
          torch.allclose(outputs.logits, expected_tensor, atol=0.05)
      )
  
imports: import copy
  import unittest
  import numpy
  import pandas
  from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TapasConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_scatter, require_tensorflow_probability, require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 9 for calling the TapasModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_classification_head() without return types