class_docstr: Handles spawning new individuals from a parent, computing an estimated gradient,
  and applying that gradient to mutate the parent.
code: def compute_all_local_rewards(self):
      return torch.tensor(
          [
              self.compute_local_reward(individual)
              for individual in self.population_tensors
          ],
          dtype=torch.float,
      )
  
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 4 for calling the EvolutionPool obj's underlying member method code with example usage: obj.compute_all_local_rewards() and returns: torch