code: def weights_init(m):
      classname = m.__class__.__name__
      if classname.find("Linear") != -1:
          nn.init.normal_(m.weight.data, 0.0, 0.02)
      elif classname.find("BatchNorm") != -1:
          nn.init.normal_(m.weight.data, 1.0, 0.02)
          nn.init.constant_(m.bias.data, 0)
  
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 1 for calling a global function with example usage: weights_init(m) without return types