class_docstr: ## Configurations
  
  This extends MNIST configurations to get the data loaders and Training and validation loop
  configurations to simplify our implementation.
class_docstr_tok: ["#","#","Configurations","This","extends","MNIST","configurations","to","get","the","data","loaders","and","Training","and","validation","loop","configurations","to","simplify","our","implementation","."]
docstr: Initializations
docstr_tok: ["Initializations"]
code: def init(self):
      """
      Initializations
      """
      self.state_modules = []
  
      hook_model_outputs(self.mode, self.generator, "generator")
      hook_model_outputs(self.mode, self.discriminator, "discriminator")
      tracker.set_scalar("loss.generator.*", True)
      tracker.set_scalar("loss.discriminator.*", True)
      tracker.set_image("generated", True, 1 / 100)
  
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 the Configs obj's underlying member method code with example usage: obj.init() without return types