docstr: Returns the learned PyTorch model.
  
  :return: The learned PyTorch model.
docstr_tok: ["Returns","the","learned","PyTorch","model",".",":","return",":","The","learned","PyTorch","model","."]
code: def get_model(self) -> "Module":
      """
      Returns the learned PyTorch model.
  
      :return: The learned PyTorch model.
      """
      state = self.get_state_dict()
      model = self.model_creator(self.config)  # type:ignore
      model_state = state["models"][0]
      model.load_state_dict(model_state)
      return model.module if hasattr(model, "module") else model
  
imports: import types
  import copy
  import logging
  import math
  from bigdl.orca.data.ray_xshards import RayXShards
  from bigdl.orca.learn.pytorch.pytorch_ray_worker import PytorchRayWorker
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.learn.pytorch.core.base_ray_estimator import BaseRayEstimator
  from bigdl.orca.learn.pytorch.utils import process_stats, check_for_failure
  from bigdl.orca.learn.pytorch.callbacks.maincallback import make_only_mainCallback
  from bigdl.orca.learn.pytorch.callbacks.tqdm import TqdmCallback, is_tqdm_exists
  from bigdl.orca.learn.pytorch.callbacks.maxsteps import MaxstepsCallback
  import ray
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import TYPE_CHECKING, Union, Optional, Callable, Dict, List
label_desc: Use image node_id 5 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj.get_model() and returns: unknown