docstr: Saves the model at the provided checkpoint.
  
  :param checkpoint: (str) Path to the target checkpoint file.
code: def save_checkpoint(self, checkpoint: str) -> str:
      """
      Saves the model at the provided checkpoint.
  
      :param checkpoint: (str) Path to the target checkpoint file.
      """
  
      # Some model might need to aggregate variables during checkpointing
      # which requires both the chief and workers to participate in the
      # allreduce communication protocol.
      # So we need to call get_state on every remote workers, otherwise
      # it might get stuck
      state_refs = [w.get_state.remote() for w in self.remote_workers]
  
      state = ray.get(state_refs[0])
  
      with open(checkpoint, "wb") as f:
          SafePickle.dump(state, f)
  
      return checkpoint
  
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 10 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint) and returns: checkpoint