code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = copy.deepcopy(inputs_dict)
  
      if model_class.__name__ == "PerceiverForMultimodalAutoencoding":
          inputs_dict[
              "subsampled_output_points"
          ] = self.model_tester.subsampling
  
      if return_labels:
          if model_class in [
              *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
              *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_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_MASKED_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 inspect
  import math
  import tempfile
  import unittest
  import warnings
  from typing import Dict, List, Tuple
  import numpy
  from datasets import load_dataset
  from transformers import PerceiverConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  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 2 for calling the PerceiverModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict