code: def _prepare_for_class(self, inputs_dict, model_class):
      inputs_dict = copy.deepcopy(inputs_dict)
  
      # hack for now until we have AutoModel classes
      if "ForMultipleChoice" in model_class.__name__:
          inputs_dict = {
              k: jnp.broadcast_to(
                  v[:, None],
                  (
                      v.shape[0],
                      self.model_tester.num_choices,
                      v.shape[-1],
                  ),
              )
              if isinstance(v, (jnp.ndarray, np.ndarray))
              else v
              for k, v in inputs_dict.items()
          }
  
      return inputs_dict
  
imports: import copy
  import inspect
  import json
  import random
  import tempfile
  import unittest
  from typing import List, Tuple
  import numpy
  import transformers
  from huggingface_hub import HfFolder, delete_repo, set_access_token
  from requests.exceptions import HTTPError
  from transformers import BertConfig, is_flax_available, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import TOKEN, USER, CaptureLogger, is_pt_flax_cross_test, is_staging_test, require_flax, torch_device
  from transformers.utils import logging
  from transformers.utils.generic import ModelOutput
label_desc: Use image node_id 1 for calling the FlaxModelTesterMixin obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class) and returns: inputs_dict