code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ) -> dict:
      inputs_dict = copy.deepcopy(inputs_dict)
  
      if model_class in get_values(
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
      ):
          inputs_dict = {
              k: tf.tile(
                  tf.expand_dims(v, 1),
                  (1, self.model_tester.num_choices)
                  + (1,) * (v.ndim - 1),
              )
              if isinstance(v, tf.Tensor) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }
  
      if return_labels:
          if model_class in get_values(
              TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
          ):
              inputs_dict["labels"] = tf.ones(
                  self.model_tester.batch_size, dtype=tf.int32
              )
          elif model_class in get_values(
              TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
          ):
              inputs_dict["labels"] = tf.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=tf.int32,
              )
              inputs_dict["aggregation_labels"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
              inputs_dict["numeric_values"] = tf.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=tf.float32,
              )
              inputs_dict["numeric_values_scale"] = tf.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=tf.float32,
              )
              inputs_dict["float_answer"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.float32
              )
          elif model_class in get_values(
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
          ):
              inputs_dict["labels"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
          elif model_class in get_values(
              TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
          ):
              inputs_dict["next_sentence_label"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
          elif model_class in [
              *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
              *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
              *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
              *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
              *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
          ]:
              inputs_dict["labels"] = tf.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=tf.int32,
              )
      return inputs_dict
  
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 1 for calling the TFTapasModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict