code: def main():
      # Init our model
      mnist_model = MNISTModel()
  
      # Init DataLoader from MNIST Dataset
      train_ds = MNIST(
          str(lab.get_data_path()),
          train=True,
          download=True,
          transform=transforms.ToTensor(),
      )
      train_loader = DataLoader(train_ds, batch_size=32)
  
      # Initialize a trainer
      trainer = pl.Trainer(
          gpus=1,
          max_epochs=3,
          progress_bar_refresh_rate=20,
          logger=LabMLLightningLogger(),
      )
  
      # Train the model ⚡
      with experiment.record(
          name="mnist_lightening", disable_screen=True
      ):
          trainer.fit(mnist_model, train_loader)
  
file_path: labml/samples/lightning/mnist.py
imports: import pytorch_lightning
  import torch
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import MNIST
  from labml import lab, experiment
  from labml.utils.lightning import LabMLLightningLogger
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types