code: def on_train_start(self) -> None:
      # make sure that the model is on GPU when training
      assert self.device == torch.device("cuda:0")
      self.start_cuda_memory = torch.cuda.memory_allocated()
  
imports: import pickle
  from unittest.mock import MagicMock, Mock
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.core.optimizer import LightningOptimizer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.strategies import SingleDeviceStrategy
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.dataloaders import CustomNotImplementedErrorDataloader
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 1 for calling the BoringModelGPU obj's underlying member method code with example usage: obj.on_train_start() without return types