code: def train(
      model,
      device,
      train_loader,
      optimizer,
      epoch,
      log_interval,
      dry_run,
  ):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % log_interval == 0:
              wandb.log(
                  {
                      "epoch": epoch,
                      "step": batch_idx * len(data),
                      "loss": loss.item(),
                  }
              )
              if dry_run:
                  break
  
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 a global function with example usage: train(model, device, train_loader, optimizer, epoch, log_interval, dry_run) without return types