docstr: Loads a model saved via `estimator.save()
  
  :param filepath: (str) Path of saved model (SavedModel or H5 file).
         It can be local/hdfs filepath
  :param custom_objects: Optional dictionary mapping names (strings) to
         custom classes or functions to be considered during deserialization.
  :param compile: Boolean, whether to compile the model after loading.
  :param options: Optional `tf.saved_model.LoadOptions` object that specifies
         options for loading from SavedModel.
code: def load(
      self,
      filepath: str,
      custom_objects: Optional[Dict] = None,
      compile: bool = True,
      options: Optional["SaveOptions"] = None,
  ) -> None:
      """
      Loads a model saved via `estimator.save()
  
      :param filepath: (str) Path of saved model (SavedModel or H5 file).
             It can be local/hdfs filepath
      :param custom_objects: Optional dictionary mapping names (strings) to
             custom classes or functions to be considered during deserialization.
      :param compile: Boolean, whether to compile the model after loading.
      :param options: Optional `tf.saved_model.LoadOptions` object that specifies
             options for loading from SavedModel.
  
      """
      self.load_params = dict(
          filepath=filepath,
          custom_objects=custom_objects,
          compile=compile,
          options=options,
      )
      if is_local_path(filepath):
          ray.get(
              [
                  worker.load_model.remote(**self.load_params)
                  for worker in self.remote_workers
              ]
          )
      else:
          ray.get(
              [
                  worker.load_remote_model.remote(**self.load_params)
                  for worker in self.remote_workers
              ]
          )
  
imports: import os
  import types
  from bigdl.orca.common import SafePickle
  import shutil
  import tempfile
  import logging
  import ray
  from bigdl.dllib.utils import log4Error
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  from bigdl.orca.data.ray_xshards import RayXShards
  from bigdl.orca.learn.dl_cluster import RayDLCluster
  from bigdl.orca.learn.tf2.tf_runner import TFRunner
  from bigdl.orca.learn.ray_estimator import Estimator
  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, make_data_creator, add_predict_to_pd_xshards
  from bigdl.orca.data.file import get_remote_file_to_local, get_remote_dir_to_local, is_file
  from bigdl.orca.data.utils import process_spark_xshards
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.ray import OrcaRayContext
  from typing import TYPE_CHECKING, Dict, List, Callable, Union, Optional
label_desc: Use image node_id 13 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.load(filepath, custom_objects, compile, options) without return types