docstr: Saves the Estimator state (including model and optimizer) or the entire model
  to the provided model_path.
  
  :param model_path: (str) Path to save the model.
  :param entire: (boolean) Whether to save the entire model. If False, saves the
         Estimator state. Default is False.
  :return:
docstr_tok: ["Saves","the","Estimator","state","(","including","model","and","optimizer",")","or","the","entire","model","to","the","provided","model_path",".",":","param","model_path",":","(","str",")","Path","to","save","the","model",".",":","param","entire",":","(","boolean",")","Whether","to","save","the","entire","model",".","If","False",",","saves","the","Estimator","state",".","Default","is","False",".",":","return",":"]
code: def save(self, model_path: str, entire: bool = False) -> str:
      """
      Saves the Estimator state (including model and optimizer) or the entire model
      to the provided model_path.
  
      :param model_path: (str) Path to save the model.
      :param entire: (boolean) Whether to save the entire model. If False, saves the
             Estimator state. Default is False.
      :return:
      """
      if is_local_path(model_path):
          if entire:
              torch.save(self.get_model(), model_path)
          else:
              torch.save(self.state_dict, model_path)
      else:
          file_name = os.path.basename(model_path)
          temp_dir = tempfile.mkdtemp()
          temp_path = os.path.join(temp_dir, file_name)
          try:
              if entire:
                  torch.save(self.get_model(), temp_path)
              else:
                  torch.save(self.state_dict, temp_path)
              put_local_file_to_remote(temp_path, model_path)
          finally:
              shutil.rmtree(temp_dir)
      return model_path
  
imports: import types
  import torch
  import copy
  import os
  import shutil
  import tempfile
  import logging
  import math
  from pyspark.sql.dataframe import DataFrame
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca.learn.pytorch.pytorch_pyspark_worker import PytorchPysparkWorker
  from bigdl.orca.learn.pytorch.utils import process_stats
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, make_data_creator, update_predict_xshards, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards
  from bigdl.orca.data import SparkXShards
  from bigdl.orca import OrcaContext
  from bigdl.orca.learn.base_estimator import BaseEstimator
  from bigdl.orca.data.file import get_remote_file_to_local, put_local_file_to_remote
  from bigdl.dllib.utils.common import get_node_and_core_number
  from bigdl.orca.learn.log_monitor import start_log_server, stop_log_server
  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
  from bigdl.orca.learn.utils import find_free_port, find_ip_and_free_port
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import TYPE_CHECKING, Union, Optional, Callable, Dict, List
  from bigdl.orca.learn.pytorch.callbacks import Callback
label_desc: Use image node_id 12 for calling the PyTorchPySparkEstimator obj's underlying member method code with example usage: obj.save(model_path, entire) and returns: model_path