code: def start(self):
      self.use_cuda = not self.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(self.seed)
  
      self.train_kwargs = {"batch_size": self.batch_size}
      self.test_kwargs = {"batch_size": self.test_batch_size}
      if self.use_cuda:
          self.cuda_kwargs = {
              "num_workers": 1,
              "pin_memory": True,
              "shuffle": True,
          }
          self.train_kwargs.update(self.cuda_kwargs)
          self.test_kwargs.update(self.cuda_kwargs)
  
      self.mnist_dir = Path("../data")
      self.next(self.setup_data)
  
imports: import os
  from pathlib import Path
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import wandb
  from metaflow import FlowSpec, Parameter, step
  from torch.optim.lr_scheduler import StepLR
  from torchvision import datasets, transforms
  from wandb.integration.metaflow import wandb_log
label_desc: Use image node_id 1 for calling the WandbPyTorchFlow obj's underlying member method code with example usage: obj.start() without return types