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","."]
code: def effective_n_jobs(self, n_jobs):
      eff_n_jobs = super(RayBackend, self).effective_n_jobs(n_jobs)
      if n_jobs == -1:
          ray_cpus = int(ray._private.state.cluster_resources()["CPU"])
          eff_n_jobs = ray_cpus
      return eff_n_jobs
  
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 3 for calling the RayBackend obj's underlying member method code with example usage: obj.effective_n_jobs(n_jobs) and returns: eff_n_jobs