code: def reset_config(self, new_config):
      if "netD_lr" in new_config:
          for param_group in self.optimizerD.param_groups:
              param_group["lr"] = new_config["netD_lr"]
      if "netG_lr" in new_config:
          for param_group in self.optimizerG.param_groups:
              param_group["lr"] = new_config["netG_lr"]
  
      self.config = new_config
      return True
  
imports: import ray
  from ray import train, tune
  from ray.tune.schedulers import PopulationBasedTraining
  import argparse
  import os
  from filelock import FileLock
  import random
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import numpy
  from common import beta1, MODEL_PATH
  from common import demo_gan, get_data_loader, plot_images, train_func, weights_init
  from common import Discriminator, Generator, Net
label_desc: Use image node_id 5 for calling the PytorchTrainable obj's underlying member method code with example usage: obj.reset_config(new_config) and returns: True