class_docstr: A simple example of an evolution pool.  The agent gets maximum reward
  as the tensor approaches [inf, -inf, inf, -inf, ...]
code: def compute_local_reward(self, individual):
      sigmoid_params = torch.nn.Sigmoid()(individual["data"])
      total_reward = torch.sum(sigmoid_params[0::2]) + torch.sum(
          1 - sigmoid_params[1::2]
      )
      return total_reward / sigmoid_params.shape[0]
  
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 1 for calling the OneMaxEvolutionPool obj's underlying member method code with example usage: obj.compute_local_reward(individual) and returns: unknown