class_docstr: Ray backend uses ray, a system for scalable distributed computing.
  More info about Ray is available here: https://docs.ray.io.
class_docstr_tok: ["Ray","backend","uses","ray",",","a","system","for","scalable","distributed","computing",".","More","info","about","Ray","is","available","here",":","https",":","\/\/docs.ray.io","."]
docstr: ``ray_remote_args`` will be used to configure Ray Actors
  making up the pool.
docstr_tok: ["``","ray_remote_args","``","will","be","used","to","configure","Ray","Actors","making","up","the","pool","."]
code: def __init__(
      self,
      nesting_level: Optional[int] = None,
      inner_max_num_threads: Optional[int] = None,
      ray_remote_args: Optional[Dict[str, Any]] = None,
      **kwargs,
  ):
      """``ray_remote_args`` will be used to configure Ray Actors
      making up the pool."""
      usage_lib.record_library_usage("util.joblib")
  
      self.ray_remote_args = ray_remote_args
      super().__init__(
          nesting_level=nesting_level,
          inner_max_num_threads=inner_max_num_threads,
          **kwargs,
      )
  
file_path: ray/python/ray/util/joblib/ray_backend.py
imports: import logging
  from typing import Any, Dict, Optional
  from joblib import Parallel
  from joblib._parallel_backends import MultiprocessingBackend
  from joblib.pool import PicklingPool
  import ray
  from ray._private.usage import usage_lib
  from ray.util.multiprocessing.pool import Pool
label_desc: Use image node_id 1 to create a new RayBackend object from inherited base classes: MultiprocessingBackend with example: obj = RayBackend(nesting_level, inner_max_num_threads, ray_remote_args)