class_docstr: A class that orchestrates the execution of the execution of a batch of explanations in parallel.
class_docstr_tok: ["A","class","that","orchestrates","the","execution","of","the","execution","of","a","batch","of","explanations","in","parallel","."]
docstr: Creates a pool of actors that can explain the rows of a dataset in parallel.
  
  Parameters
  ----------
  See constructor documentation.
code: def create_parallel_pool(
      self,
      explainer_type: Any,
      explainer_init_args: Tuple,
      explainer_init_kwargs: dict,
  ):
      """
      Creates a pool of actors that can explain the rows of a dataset in parallel.
  
      Parameters
      ----------
      See constructor documentation.
      """
  
      handles = [
          ray.remote(explainer_type) for _ in range(self.n_processes)
      ]
      workers = [
          handle.remote(*explainer_init_args, **explainer_init_kwargs)
          for handle in handles
      ]
      return ray.util.ActorPool(workers)
  
imports: import copy
  import logging
  from functools import partial
  from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
  import numpy
  from scipy import sparse
  import ray
label_desc: Use image node_id 7 for calling the DistributedExplainer obj's underlying member method code with example usage: obj.create_parallel_pool(explainer_type, explainer_init_args, explainer_init_kwargs) and returns: ray