code: def test_output_pretrained(self):
      model = PriorTransformer.from_pretrained(
          "hf-internal-testing/prior-dummy"
      )
      model = model.to(torch_device)
  
      if hasattr(model, "set_default_attn_processor"):
          model.set_default_attn_processor()
  
      input = self.get_dummy_seed_input()
  
      with torch.no_grad():
          output = model(**input)[0]
  
      output_slice = output[0, :5].flatten().cpu()
      print(output_slice)
  
      # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
      # the expected output slices are not the same for CPU and GPU.
      expected_output_slice = torch.tensor(
          [-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]
      )
      self.assertTrue(
          torch_all_close(
              output_slice, expected_output_slice, rtol=1e-2
          )
      )
  
imports: import gc
  import inspect
  import unittest
  import torch
  from parameterized import parameterized
  from diffusers import PriorTransformer
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, slow, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin
label_desc: Use image node_id 8 for calling the PriorTransformerTests obj's underlying member method code with example usage: obj.test_output_pretrained() without return types