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_QUESTION_ANSWERING_MAPPING
          ):
              inputs_dict["start_positions"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
              inputs_dict["end_positions"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
          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_TOKEN_CLASSIFICATION_MAPPING
          ):
              inputs_dict["labels"] = tf.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.text_seq_length,
                  ),
                  dtype=tf.int32,
              )
  
      return inputs_dict
  
imports: import copy
  import inspect
  import unittest
  import numpy
  from transformers import is_tf_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the TFLayoutLMv3ModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict