code: def test_trainer_jittor(
      driver,
      device,
      callbacks,
      n_epochs=3,
  ):
      model = JittorNormalModel_Classification(
          num_labels=TrainJittorConfig.num_labels,
          feature_dimension=TrainJittorConfig.feature_dimension,
      )
      optimizer = nn.SGD(model.parameters(), lr=TrainJittorConfig.lr)
      train_dataloader = JittorDataLoader(
          dataset=JittorRandomMaxDataset(
              20, TrainJittorConfig.feature_dimension
          ),
          batch_size=TrainJittorConfig.batch_size,
          shuffle=True,
          # num_workers=4,
      )
      val_dataloader = JittorDataLoader(
          dataset=JittorRandomMaxDataset(
              12, TrainJittorConfig.feature_dimension
          ),
          batch_size=TrainJittorConfig.batch_size,
          shuffle=True,
          # num_workers=4,
      )
      test_dataloader = JittorDataLoader(
          dataset=JittorRandomMaxDataset(
              12, TrainJittorConfig.feature_dimension
          ),
          batch_size=TrainJittorConfig.batch_size,
          shuffle=True,
          # num_workers=4,
      )
      metrics = {"acc": Accuracy()}
  
      trainer = Trainer(
          model=model,
          driver=driver,
          device=device,
          optimizers=optimizer,
          train_dataloader=train_dataloader,
          evaluate_dataloaders=val_dataloader,
          validate_every=-1,
          evaluate_fn="evaluate_step",
          input_mapping=None,
          output_mapping=None,
          metrics=metrics,
          n_epochs=n_epochs,
          callbacks=callbacks,
          # progress_bar="rich"
      )
      trainer.run()
  
      evaluator = Evaluator(
          model=model,
          driver=driver,
          dataloaders=test_dataloader,
          evaluate_fn="evaluate_step",
          metrics=metrics,
      )
      metric_results = evaluator.run()
  
imports: import pytest
  from fastNLP.core.callbacks import callback
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.core.controllers.trainer import Evaluator
  from fastNLP.core.metrics.accuracy import Accuracy
  from fastNLP.core.callbacks.progress_callback import RichCallback
  from fastNLP.core.dataloaders.jittor_dataloader.fdl import JittorDataLoader
  from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
label_desc: Use image node_id 1 for calling a global function with example usage: test_trainer_jittor(driver, device, callbacks, n_epochs) without return types