code: def default_target_fcn(
      actor: Any, instances: tuple, kwargs: Optional[Dict] = None
  ):
      """
      A target function that is executed in parallel given an actor pool. Its arguments must be an actor and a batch of
      values to be processed by the actor. Its role is to execute distributed computations when an actor is available.
  
      Parameters
      ----------
      actor
          A `ray` actor. This is typically a class decorated with the `@ray.remote decorator`, that has been subsequently
          instantiated using ``cls.remote(*args, **kwargs)``.
      instances
          A `(batch_index, batch)` tuple containing the batch of instances to be explained along with a batch index.
      kwargs
          A list of keyword arguments for the actor `get_explanation` method.
  
      Returns
      -------
      A future that can be used to later retrieve the results of a distributed computation.
  
      Notes
      -----
      This function can be customized (e.g., if one does not desire to wrap the explainer such that it has
      `get_explanation` method. The customized function should be called `*_target_fcn` with the wildcard being replaced
      by the name of the explanation method (e.g., `cem`, `cfproto`, etc). The same name should be added to the
      `distributed_opts` dictionary passed by the user prior to instantiating the `DistributedExplainer`.
  
      """
  
      if kwargs is None:
          kwargs = {}
  
      return actor.get_explanation.remote(instances, **kwargs)
  
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 2 for calling a global function with example usage: default_target_fcn(actor, instances, kwargs) and returns: actor