code: def driver():
      warnings.filterwarnings("ignore")
      # Dependencies for deploying the model
      pytorch_index = "https://download.pytorch.org/whl/"
      pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
      deps = [
          "cloudpickle=={}".format(cloudpickle.__version__),
          pytorch_index + pytorch_version,
          "torchvision=={}".format(torchvision.__version__),
          "Pillow=={}".format("6.0.0"),
      ]
      with mlflow.start_run() as run:
          model = Net().to(device)
          optimizer = optim.SGD(
              model.parameters(), lr=args.lr, momentum=args.momentum
          )
          for epoch in range(1, args.epochs + 1):
              train(args, model, device, train_loader, optimizer, epoch)
              test(args, model, device, test_loader)
          # Log model to run history using MLflow
          if args.save_model:
              model_env = _mlflow_conda_env(additional_pip_deps=deps)
              mlflow.pytorch.log_model(
                  model, "model", conda_env=model_env
              )
      return run
  
file_path: MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py
imports: import mlflow
  import mlflow.pytorch
  from mlflow.utils.environment import _mlflow_conda_env
  import warnings
  import cloudpickle
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision
  from torchvision import datasets, transforms
label_desc: Use image node_id 3 for calling a global function with example usage: driver() and returns: run