class_docstr: Handles spawning new individuals from a parent, computing an estimated gradient,
  and applying that gradient to mutate the parent.
code: def populate_children(self, iteration: int):
      torch.manual_seed(iteration * MAX_RNG_SEED + self.seed)
      for individual in self.population_tensors:
          for tensor_name, parent_tensor in self.parent_tensors.items():
              individual_tensor = individual[tensor_name]
  
              individual_tensor.normal_(
                  0, self.es_params.mutation_power
              )
              individual_tensor.add_(parent_tensor)
  
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 2 for calling the EvolutionPool obj's underlying member method code with example usage: obj.populate_children(iteration) without return types