code: def test_train_and_evaluate(self):
      orca_estimator = get_estimator(workers_per_node=2)
      train_dataset = train_data_creator()
      val_dataset = val_data_creator()
  
      start_val_stats = orca_estimator.evaluate(
          data=val_dataset,
          batch_size=32,
          label_cols="y",
          feature_cols=["x"],
      )
      print(start_val_stats)
  
      train_stats = orca_estimator.fit(
          data=train_dataset,
          epochs=2,
          batch_size=32,
          label_cols="y",
          feature_cols=["x"],
      )
  
      end_val_stats = orca_estimator.evaluate(
          data=val_dataset,
          batch_size=32,
          label_cols="y",
          feature_cols=["x"],
      )
      print(end_val_stats)
  
      assert orca_estimator.get_model()
      dloss = end_val_stats["val_loss"] - start_val_stats["val_loss"]
      print(f"dLoss: {dloss}")
      assert dloss < 0, "training sanity check failed. loss increased!"
  
imports: import pytest
  import logging
  from unittest import TestCase
  import ray
  from ray.data import Dataset
  import torch.nn
  import torch.optim
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import MSE
label_desc: Use image node_id 3 for calling the TestPytorchEstimator obj's underlying member method code with example usage: obj.test_train_and_evaluate() without return types