code: def test_brain(device):
      import torch
      from speechbrain.core import Brain, Stage
      from torch.optim import SGD
  
      model = torch.nn.Linear(
          in_features=10, out_features=10, device=device
      )
  
      class SimpleBrain(Brain):
          def compute_forward(self, batch, stage):
              return self.modules.model(batch[0])
  
          def compute_objectives(self, predictions, batch, stage):
              return torch.nn.functional.l1_loss(predictions, batch[1])
  
      brain = SimpleBrain(
          {"model": model},
          lambda x: SGD(x, 0.1),
          run_opts={"device": device},
      )
  
      inputs = torch.rand(10, 10, device=device)
      targets = torch.rand(10, 10, device=device)
      train_set = ([inputs, targets],)
      valid_set = ([inputs, targets],)
  
      start_output = brain.compute_forward(inputs, Stage.VALID)
      start_loss = brain.compute_objectives(
          start_output, targets, Stage.VALID
      )
      brain.fit(
          epoch_counter=range(10),
          train_set=train_set,
          valid_set=valid_set,
      )
      end_output = brain.compute_forward(inputs, Stage.VALID)
      end_loss = brain.compute_objectives(
          end_output, targets, Stage.VALID
      )
      assert end_loss < start_loss
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_brain(device) and returns: self, torch