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 = sum(self.model_tester.depths) + 1
          self.assertEqual(len(hidden_states), expected_num_layers)
  
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-3:]),
              [
                  self.model_tester.batch_size,
                  (self.model_tester.image_size // 4) ** 2,
                  self.model_tester.image_size // 4,
              ],
          )
  
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      for model_class in self.all_model_classes:
          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 unittest
  from transformers import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_accelerate, require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 7 for calling the PvtModelTest obj's underlying member method code with example usage: obj.test_hidden_states_output() without return types