docstr: Load existing checkpoint. To load a specific checkpoint, please provide both `version` and
  `perfix`. If `version` is None, then the latest checkpoint will be loaded.
  
  :param path: Path to the existing checkpoint (or directory containing Orca checkpoint
         files).
  :param version: checkpoint version, which is the suffix of model.* file, i.e., for
         modle.4 file, the version is 4. If it is None, then load the latest checkpoint.
  :param prefix: optimMethod prefix, for example 'optimMethod-TorchModelf53bddcc'.
  :return:
docstr_tok: ["Load","existing","checkpoint",".","To","load","a","specific","checkpoint",",","please","provide","both","`","version","`","and","`","perfix","`",".","If","`","version","`","is","None",",","then","the","latest","checkpoint","will","be","loaded",".",":","param","path",":","Path","to","the","existing","checkpoint","(","or","directory","containing","Orca","checkpoint","files",")",".",":","param","version",":","checkpoint","version",",","which","is","the","suffix","of","model",".","*","file",",","i.e.",",","for","modle.4","file",",","the","version","is","4",".","If","it","is","None",",","then","load","the","latest","checkpoint",".",":","param","prefix",":","optimMethod","prefix",",","for","example","'optimMethod-TorchModelf53bddcc","'",".",":","return",":"]
code: def load_orca_checkpoint(self, path, version=None, prefix=None):
      """
      Load existing checkpoint. To load a specific checkpoint, please provide both `version` and
      `perfix`. If `version` is None, then the latest checkpoint will be loaded.
  
      :param path: Path to the existing checkpoint (or directory containing Orca checkpoint
             files).
      :param version: checkpoint version, which is the suffix of model.* file, i.e., for
             modle.4 file, the version is 4. If it is None, then load the latest checkpoint.
      :param prefix: optimMethod prefix, for example 'optimMethod-TorchModelf53bddcc'.
      :return:
      """
      import os
      from bigdl.nn.layer import Model
      from bigdl.optim.optimizer import OptimMethod
      from zoo.orca.learn.utils import find_latest_checkpoint
      from zoo.pipeline.api.torch import TorchModel
  
      if version is None:
          path, prefix, version = find_latest_checkpoint(
              path, model_type="pytorch"
          )
          if path is None:
              raise ValueError(
                  "Cannot find PyTorch checkpoint, please check your checkpoint"
                  " path."
              )
      else:
          assert prefix is not None, (
              "You should provide optimMethod prefix, "
              "for example 'optimMethod-TorchModelf53bddcc'"
          )
  
      try:
          loaded_model = Model.load(
              os.path.join(path, "model.{}".format(version))
          )
          self.model = TorchModel.from_value(loaded_model.value)
          self.optimizer = OptimMethod.load(
              os.path.join(path, "{}.{}".format(prefix, version))
          )
      except Exception as e:
          raise ValueError(
              "Cannot load PyTorch checkpoint, please check your checkpoint path "
              "and checkpoint type." + str(e)
          )
      self.estimator = SparkEstimator(
          self.model, self.optimizer, self.model_dir
      )
  
imports: from zoo.common.utils import enable_multi_fs_load, enable_multi_fs_save
  from zoo.orca.data.utils import row_to_sample, xshard_to_sample
  from zoo.orca.learn.utils import convert_predict_rdd_to_dataframe, bigdl_metric_results_to_dict, process_xshards_of_pandas_dataframe
  from zoo.pipeline.estimator.estimator import Estimator
  from zoo.orca.learn.ray_estimator import Estimator
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.learn.optimizers import Optimizer, SGD
  from zoo.orca.learn.metrics import Accuracy
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch, OptimMethod
  from zoo.feature.common import FeatureSet
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from pyspark.sql import DataFrame
  import warnings
  import torch
  import types
label_desc: Use image node_id 12 for calling the PyTorchSparkEstimator obj's underlying member method code with example usage: obj.load_orca_checkpoint(path, version, prefix) without return types