class_docstr: ClusterResolver for system with Slurm workload manager.
  
  This is an implementation of ClusterResolver for Slurm clusters. This allows
  the specification of jobs and task counts, number of tasks per node, number
  of GPUs on each node and number of GPUs for each task. It retrieves system
  attributes by Slurm environment variables, resolves allocated computing node
  names, constructs a cluster and returns a ClusterResolver object which can be
  used for distributed TensorFlow.
class_docstr_tok: ["ClusterResolver","for","system","with","Slurm","workload","manager",".","This","is","an","implementation","of","ClusterResolver","for","Slurm","clusters",".","This","allows","the","specification","of","jobs","and","task","counts",",","number","of","tasks","per","node",",","number","of","GPUs","on","each","node","and","number","of","GPUs","for","each","task",".","It","retrieves","system","attributes","by","Slurm","environment","variables",",","resolves","allocated","computing","node","names",",","constructs","a","cluster","and","returns","a","ClusterResolver","object","which","can","be","used","for","distributed","TensorFlow","."]
docstr: Returns the rank of the current task in range [0, num_tasks).
docstr_tok: ["Returns","the","rank","of","the","current","task","in","range","[","0",",","num_tasks",")","."]
code: def _resolve_own_rank(self):
      """Returns the rank of the current task in range [0, num_tasks)."""
      return int(_get_slurm_var("PROCID"))
  
imports: import os
  import re
  import subprocess
  from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver
  from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url
  from tensorflow.python.training.server_lib import ClusterSpec
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling the SlurmClusterResolver obj's underlying member method code with example usage: obj._resolve_own_rank() and returns: int