code: def test_hidden_states_output(self):
      def check_hidden_states_output(inputs_dict, config, model_class):
          model = model_class(config)
          model.to(torch_device)
          model.eval()
  
          with torch.no_grad():
              outputs = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )
  
          hidden_states = outputs.hidden_states
  
          expected_num_layers = (
              self.model_tester.num_blocks
              * self.model_tester.num_self_attends_per_block
              + 1
          )
          self.assertEqual(len(hidden_states), expected_num_layers)
  
          seq_length = self.model_tester.num_latents
  
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]),
              [seq_length, self.model_tester.d_latents],
          )
  
      for model_class in self.all_model_classes:
          (
              config,
              inputs_dict,
          ) = self.model_tester.prepare_config_and_inputs_for_model_class(
              model_class
          )
  
          inputs_dict["output_hidden_states"] = True
          check_hidden_states_output(inputs_dict, config, model_class)
  
          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          config.output_hidden_states = True
  
          check_hidden_states_output(inputs_dict, config, model_class)
  
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 14 for calling the PerceiverModelTest obj's underlying member method code with example usage: obj.test_hidden_states_output() without return types