code: def test_lightining_efficientdet_logs_losses_during_training_step(
      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
  
          light_model.training_step(batch, 0)
  
          assert sorted(light_model.logs.keys()) == sorted(
              ["train_loss", "train_box_loss", "train_class_loss"]
          )
  
imports: import pytest
  import random
  from icevision.all import *
label_desc: Use image node_id 4 for calling a global function with example usage: test_lightining_efficientdet_logs_losses_during_training_step(fridge_efficientdet_dls, fridge_efficientdet_model, light_model_cls) without return types