code: def test_training(self):
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
      model = self.block_class(**init_dict)
      model.to(torch_device)
      model.train()
      output = model(**inputs_dict)
  
      if isinstance(output, Tuple):
          output = output[0]
  
      device = torch.device(torch_device)
      noise = randn_tensor(output.shape, device=device)
      loss = torch.nn.functional.mse_loss(output, noise)
      loss.backward()
  
file_path: diffusers/tests/models/test_unet_blocks_common.py
imports: from typing import Tuple
  import torch
  from diffusers.utils.testing_utils import floats_tensor, require_torch, require_torch_accelerator_with_training, torch_all_close, torch_device
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 6 for calling the UNetBlockTesterMixin obj's underlying member method code with example usage: obj.test_training() without return types