code: def __init__(
      self,
      model_creator,
      optimizer_creator,
      loss_creator,
      metrics=None,
      scheduler_creator=None,
      config=None,
      init_func=None,  # Init the distributed environment for MPI if any
      hosts=None,
      workers_per_node=1,
      env=None,
  ):
      self.dir = os.getcwd()
      self.mpi_runner = MPIRunner(
          hosts=hosts, processes_per_node=workers_per_node, env=env
      )
      with open("saved_mpi_estimator.pkl", "wb") as f:
          cloudpickle.dump(
              (
                  model_creator,
                  optimizer_creator,
                  loss_creator,
                  metrics,
                  scheduler_creator,
                  config,
                  init_func,
              ),
              f,
          )
      self.mpi_runner.scp_file("saved_mpi_estimator.pkl", self.dir)
      # Need to put mpi_train.py in the current directory so that the PYTHONPATH is the same.
      train_file = os.path.abspath(__file__ + "/../mpi_train.py")
      p = subprocess.Popen(["cp", train_file, self.dir])
      os.waitpid(p.pid, 0)
      self.mpi_runner.scp_file(train_file, self.dir)
  
imports: import os
  import types
  import subprocess
  import cloudpickle
  from pyspark.sql import DataFrame
  from torch.utils.data import Dataset, DataLoader
  from zoo.util.utils import get_node_ip
  from zoo.orca.learn.mpi.mpi_runner import MPIRunner
  from zoo.orca.learn.mpi.utils import *
label_desc: Use image node_id 1 to create a new MPIEstimator object  with example: obj = MPIEstimator(model_creator, optimizer_creator, loss_creator, metrics, scheduler_creator, config, init_func, hosts, workers_per_node, env)