docstr: Load tensorflow keras model weights from the provided path.
  param filepath: String, path to the weights file to load. For weight files in TensorFlow
        format, this is the file prefix (the same as was passed to save_weights). This can
        also be a path to a SavedModel saved from model.save.
  param by_name: Boolean, whether to load weights by name or by topological order.
        Only topological loading is supported for weight files in TensorFlow format.
  param skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch
        in the number of weights, or a mismatch in the shape of the weight
        (only valid when by_name=True).
  param options: Optional tf.train.CheckpointOptions object that specifies options for loading
        weights.
  :return:
code: def load_weights(
      self,
      filepath=str,
      by_name: bool = False,
      skip_mismatch: bool = False,
      options: Optional["SaveOptions"] = None,
  ) -> None:
      """
      Load tensorflow keras model weights from the provided path.
      param filepath: String, path to the weights file to load. For weight files in TensorFlow
            format, this is the file prefix (the same as was passed to save_weights). This can
            also be a path to a SavedModel saved from model.save.
      param by_name: Boolean, whether to load weights by name or by topological order.
            Only topological loading is supported for weight files in TensorFlow format.
      param skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch
            in the number of weights, or a mismatch in the shape of the weight
            (only valid when by_name=True).
      param options: Optional tf.train.CheckpointOptions object that specifies options for loading
            weights.
      :return:
      """
      params = dict(
          filepath=filepath,
          by_name=by_name,
          skip_mismatch=skip_mismatch,
          options=options,
      )
      if is_local_path(filepath):
          ray.get(
              [
                  worker.load_weights.remote(**params)
                  for worker in self.remote_workers
              ]
          )
      else:
          ray.get(
              [
                  worker.load_remote_weights.remote(**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 15 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.load_weights(filepath, by_name, skip_mismatch, options) without return types