code: def test_output(self, expected_slice):
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
      unet_block = self.block_class(**init_dict)
      unet_block.to(torch_device)
      unet_block.eval()
  
      with torch.no_grad():
          output = unet_block(**inputs_dict)
  
      if isinstance(output, Tuple):
          output = output[0]
  
      self.assertEqual(output.shape, self.output_shape)
  
      output_slice = output[0, -1, -3:, -3:]
      expected_slice = torch.tensor(expected_slice).to(torch_device)
      assert torch_all_close(
          output_slice.flatten(), expected_slice, atol=5e-3
      )
  
file_path: llmga/llmga/diffusers/tests/models/test_unet_blocks_common.py
imports: import unittest
  from typing import Tuple
  import torch
  from diffusers.utils.testing_utils import floats_tensor, require_torch, torch_all_close, torch_device
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 5 for calling the UNetBlockTesterMixin obj's underlying member method code with example usage: obj.test_output(expected_slice) without return types