code: def train(dataset, epochs=NUM_EPOCHS, device="cpu"):
      print("Training using %s." % device)
      train_sampler = torch.utils.data.RandomSampler(dataset)
      train_loader = torch.utils.data.DataLoader(
          dataset,
          batch_size=10,
          sampler=train_sampler,
          worker_init_fn=_dataloader_init_fn,
      )
      model = models.SimpleConvNet()
      optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
      loss_function = nn.CrossEntropyLoss()
      model = model.to(device)
      for epoch in range(epochs):
          train_epoch(
              model,
              optimizer,
              loss_function,
              train_loader,
              epoch,
              device,
          )
      return model
  
file_path: BentoML/examples/pytorch_mnist/train.py
imports: import argparse
  import os
  import random
  import model
  import numpy
  import torch
  from sklearn.model_selection import KFold
  from torch import nn
  from torchvision import transforms
  from torchvision.datasets import MNIST
  import bentoml
label_desc: Use image node_id 6 for calling a global function with example usage: train(dataset, epochs, device) and returns: model