class_docstr: Handles spawning new individuals from a parent, computing an estimated gradient,
  and applying that gradient to mutate the parent.
docstr: Given an individual as a list of tensors, return the reward
  of this policy
docstr_tok: ["Given","an","individual","as","a","list","of","tensors",",","return","the","reward","of","this","policy"]
code: def compute_local_reward(self, individual):
      """
      Given an individual as a list of tensors, return the reward
      of this policy
      """
      raise NotImplementedError()
  
file_path: ReAgent/reagent/training/gradient_free/evolution_pool.py
imports: import logging
  from typing import Dict, List
  import torch
  import torch.fb.rendezvous.zeus
  import torch.nn
  import torch.optim
  from reagent.core.parameters import EvolutionParameters
label_desc: Use image node_id 5 for calling the EvolutionPool obj's underlying member method code with example usage: obj.compute_local_reward(individual) without return types