docstr: Load checkpoint from a file or URI. The filename should either be a
  local path on driver or a HDFS path
  
  Args:
      filename (str): Local path on Driver or HDFS path.
      map_location (str): Same as :func:`torch.load`.
      strict (bool): Whether to allow different params for the model and
          checkpoint.
      revise_keys (list): A list of customized keywords to modify the
          state_dict in checkpoint. Each item is a (pattern, replacement)
          pair of the regular expression operations. Default: strip
          the prefix 'module.' by [(r'^module\.', '')].
  
  Returns:
      None
docstr_tok: ["Load","checkpoint","from","a","file","or","URI",".","The","filename","should","either","be","a","local","path","on","driver","or","a","HDFS","path","Args",":","filename","(","str",")",":","Local","path","on","Driver","or","HDFS","path",".","map_location","(","str",")",":","Same","as",":","func",":","`","torch.load","`",".","strict","(","bool",")",":","Whether","to","allow","different","params","for","the","model","and","checkpoint",".","revise_keys","(","list",")",":","A","list","of","customized","keywords","to","modify","the","state_dict","in","checkpoint",".","Each","item","is","a","(","pattern",",","replacement",")","pair","of","the","regular","expression","operations",".","Default",":","strip","the","prefix","'module",".","'","by","[","(","r'^module\\",".","'",",","``",")","]",".","Returns",":","None"]
code: def load_checkpoint(
      self,
      filename: str,
      map_location: Union[str, Callable] = "cpu",
      strict: bool = False,
      revise_keys: List = [(r"^module.", "")],
  ) -> None:
      """Load checkpoint from a file or URI. The filename should either be a
      local path on driver or a HDFS path
  
      Args:
          filename (str): Local path on Driver or HDFS path.
          map_location (str): Same as :func:`torch.load`.
          strict (bool): Whether to allow different params for the model and
              checkpoint.
          revise_keys (list): A list of customized keywords to modify the
              state_dict in checkpoint. Each item is a (pattern, replacement)
              pair of the regular expression operations. Default: strip
              the prefix 'module.' by [(r'^module\\.', '')].
  
      Returns:
          None
      """
      from bigdl.dllib.utils.file_utils import is_local_path
  
      if is_local_path(filename):
          self.load(filename)
      else:
          params = dict(
              filename=filename,
              map_location=map_location,
              strict=strict,
              revise_keys=revise_keys,
          )
          results = [
              worker.load_checkpoint.remote(**params)
              for worker in self.remote_workers
          ]
          ray.get(results)
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/experimential/mmcv/mmcv_ray_estimator.py
imports: import types
  import copy
  import ray
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.learn.pytorch.core.base_ray_estimator import BaseRayEstimator
  from bigdl.orca.learn.pytorch.experimential.mmcv.mmcv_ray_runner import MMCVRayEpochRunner
  from bigdl.orca.learn.pytorch.utils import process_stats
  from typing import Dict, List, Optional, Tuple, Callable, Union
label_desc: Use image node_id 7 for calling the MMCVRayEstimator obj's underlying member method code with example usage: obj.load_checkpoint(filename, map_location, strict, revise_keys) without return types