code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = copy.deepcopy(inputs_dict)
      if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
          inputs_dict = {
              k: v.unsqueeze(1)
              .expand(-1, self.model_tester.num_choices, -1)
              .contiguous()
              if isinstance(v, torch.Tensor) and v.ndim > 1
              else v
              for k, v in inputs_dict.items()
          }
  
      if return_labels:
          if model_class in get_values(
              MODEL_FOR_MULTIPLE_CHOICE_MAPPING
          ):
              inputs_dict["labels"] = torch.ones(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class in get_values(
              MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
          ):
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["aggregation_labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["numeric_values"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.float,
                  device=torch_device,
              )
              inputs_dict["numeric_values_scale"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.float,
                  device=torch_device,
              )
              inputs_dict["float_answer"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.float,
                  device=torch_device,
              )
          elif model_class in [
              *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
              *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
          ]:
              inputs_dict["labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class in [
              *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
              *get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
              *get_values(MODEL_FOR_MASKED_LM_MAPPING),
              *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
          ]:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
      return inputs_dict
  
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_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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the TapasModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict