code: def test_run_with_train_step(self):
      model = Model()
      optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
      cfg = dict(
          model=model,
          optimizer=optimizer,
          batch_processor=None,
          lr_config=dict(policy="step", step=[2, 3]),
          optimizer_config=dict(grad_clip=None),
          checkpoint_config=None,
          log_config=dict(
              interval=4, hooks=[dict(type="TextLoggerHook")]
          ),
      )
      estimator = get_estimator(runner_creator, cfg)
      epoch_stats = estimator.run(
          [train_dataloader_creator], [("train", 1)]
      )
      self.assertEqual(len(epoch_stats), MAX_EPOCH)
  
      start_stats = epoch_stats[0]
      end_stats = epoch_stats[-1]
      self.assertEqual(start_stats["num_samples"], NUM_SAMPLES)
      self.assertEqual(end_stats["num_samples"], NUM_SAMPLES)
  
      dloss = end_stats["loss"] - start_stats["loss"]
      print(f"dLoss: {dloss}")
      assert dloss < 0
  
      if os.path.exists(TEMP_WORK_DIR):
          shutil.rmtree(TEMP_WORK_DIR)
  
imports: import pytest
  import unittest
  import os
  import tempfile
  import shutil
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from mmcv.runner import EpochBasedRunner
  from mmcv.runner import DistEvalHook
  from mmcv.utils import get_logger
  from bigdl.orca.learn.pytorch import Estimator
label_desc: Use image node_id 1 for calling the TestMMCVRayEstimator obj's underlying member method code with example usage: obj.test_run_with_train_step() without return types