code: def get_params():
      # Training settings
      parser = argparse.ArgumentParser(
          description="PyTorch MNIST Example"
      )
      parser.add_argument(
          "--data_dir",
          type=str,
          default="./data",
          help="data directory",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=64,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.01,
          metavar="LR",
          help="learning rate (default: 0.01)",
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.5,
          metavar="M",
          help="SGD momentum (default: 0.5)",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=1,
          metavar="N",
          help="number of epochs to train (default: 1)",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=1000,
          metavar="N",
          help="how many batches to wait before logging training status",
      )
  
      parser.add_argument(
          "--save_checkpoint_dir",
          type=str,
          help="where to save checkpoint of this trial",
      )
      parser.add_argument(
          "--load_checkpoint_dir",
          type=str,
          help="where to load the model",
      )
  
      args, _ = parser.parse_known_args()
      return args
  
file_path: nni/examples/trials/mnist-pbt-tuner-pytorch/mnist.py
imports: import argparse
  import logging
  import os
  import nni
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
label_desc: Use image node_id 6 for calling a global function with example usage: get_params() and returns: args