code: def cross_validate(
      dataset, epochs=NUM_EPOCHS, k_folds=K_FOLDS, device="cpu"
  ):
      results = {}
  
      # Define the K-fold Cross Validator
      kfold = KFold(n_splits=k_folds, shuffle=True)
  
      print("--------------------------------")
  
      # K-fold Cross Validation model evaluation
      for fold, (train_ids, test_ids) in enumerate(
          kfold.split(dataset)
      ):
          print(f"FOLD {fold}")
          print("--------------------------------")
  
          # Sample elements randomly from a given list of ids, no replacement.
          train_subsampler = torch.utils.data.SubsetRandomSampler(
              train_ids
          )
          test_subsampler = torch.utils.data.SubsetRandomSampler(
              test_ids
          )
  
          # Define data loaders for training and testing data in this fold
          train_loader = torch.utils.data.DataLoader(
              dataset,
              batch_size=10,
              sampler=train_subsampler,
              worker_init_fn=_dataloader_init_fn,
          )
          test_loader = torch.utils.data.DataLoader(
              dataset,
              batch_size=10,
              sampler=test_subsampler,
              worker_init_fn=_dataloader_init_fn,
          )
  
          # Train this fold
          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=device,
              )
  
          # Evaluation for this fold
          correct, total = test_model(model, test_loader, device)
          print(
              "Accuracy for fold %d: %d %%"
              % (fold, 100.0 * correct / total)
          )
          print("--------------------------------")
          results[fold] = 100.0 * (correct / total)
  
      # Print fold results
      print(f"K-FOLD CROSS VALIDATION RESULTS FOR {K_FOLDS} FOLDS")
      print("--------------------------------")
      sum_ = 0.0
      for key, value in results.items():
          print(f"Fold {key}: {value} %")
          sum_ += value
  
      print(f"Average: {sum_/len(results.items())} %")
  
      return results
  
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 5 for calling a global function with example usage: cross_validate(dataset, epochs, k_folds, device) and returns: results