code: def get_estimator(
      workers_per_node=2,
      model_fn=model_creator,
      sync_stats=False,
      log_level=logging.INFO,
      loss=nn.MSELoss(),
      optimizer=optim_creator,
  ):
      estimator = Estimator.from_torch(
          model=model_fn,
          optimizer=optimizer,
          loss=loss,
          metrics=[MSE()],
          config={"lr": 1e-2},
          workers_per_node=workers_per_node,
          backend="ray",
          sync_stats=sync_stats,
          log_level=log_level,
      )
      return estimator
  
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 5 for calling a global function with example usage: get_estimator(workers_per_node, model_fn, sync_stats, log_level, loss, optimizer) and returns: estimator