code: def _discriminator_optimizer(c: Configs):
      opt_conf = OptimizerConfigs()
      opt_conf.optimizer = "Adam"
      opt_conf.parameters = c.discriminator.parameters()
      opt_conf.learning_rate = 2.5e-4
      # Setting exponent decay rate for first moment of gradient,
      # $\beta_1$ to `0.5` is important.
      # Default of `0.9` fails.
      opt_conf.betas = (0.5, 0.999)
      return opt_conf
  
imports: from typing import Any
  import torch
  import torch.nn
  import torch.utils.data
  from torchvision import transforms
  from labml import tracker, monit, experiment
  from labml.configs import option, calculate
  from labml_helpers.datasets.mnist import MNISTConfigs
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
  from labml_helpers.optimizer import OptimizerConfigs
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
  from labml_nn.gan.original import DiscriminatorLogitsLoss, GeneratorLogitsLoss
label_desc: Use image node_id 3 for calling a global function with example usage: _discriminator_optimizer(c) and returns: opt_conf