code: def test_lightining_efficientdet_training_step_returns_loss(
      fridge_efficientdet_dls,
      fridge_efficientdet_model,
      light_model_cls,
  ):
      with torch.set_grad_enabled(True):
          train_dl, _ = fridge_efficientdet_dls
          light_model = light_model_cls(
              model=fridge_efficientdet_model, metrics=None
          )
          for batch in train_dl:
              break
          expected_loss = random.randint(0, 1000)
  
          def fake_compute_loss(self, *args):
              return expected_loss
  
          light_model.compute_loss = fake_compute_loss
  
          loss = light_model.training_step(batch, 0)
  
          assert loss == expected_loss
  
imports: import pytest
  import random
  from icevision.all import *
label_desc: Use image node_id 3 for calling a global function with example usage: test_lightining_efficientdet_training_step_returns_loss(fridge_efficientdet_dls, fridge_efficientdet_model, light_model_cls) and returns: expected_loss