class_docstr: ## Configurations
class_docstr_tok: ["#","#","Configurations"]
docstr: ## Train model
docstr_tok: ["#","#","Train","model"]
code: def train(self):
      """
      ## Train model
      """
  
      # Loop for `training_steps`
      for i in monit.loop(self.training_steps):
          # Take a training step
          self.step(i)
          #
          if (i + 1) % self.log_generated_interval == 0:
              tracker.new_line()
  
imports: import math
  from pathlib import Path
  from typing import Iterator, Tuple
  import torch
  import torch.utils.data
  import torchvision
  from PIL import Image
  from labml import tracker, lab, monit, experiment
  from labml.configs import BaseConfigs
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.train_valid import ModeState, hook_model_outputs
  from labml_nn.gan.stylegan import Discriminator, Generator, MappingNetwork, GradientPenalty, PathLengthPenalty
  from labml_nn.gan.wasserstein import DiscriminatorLoss, GeneratorLoss
  from labml_nn.utils import cycle_dataloader
label_desc: Use image node_id 6 for calling the Configs obj's underlying member method code with example usage: obj.train() without return types