code: def parse_args():
      parser = argparse.ArgumentParser(description="train mnist")
      parser.add_argument(
          "--log_path",
          type=str,
          required=True,
          help="dir to place tensorboard logs from all trials",
      )
      parser.add_argument(
          "--hidden_size_1",
          type=int,
          required=True,
          help="hidden size layer 1",
      )
      parser.add_argument(
          "--hidden_size_2",
          type=int,
          required=True,
          help="hidden size layer 2",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          required=True,
          help="learning rate",
      )
      parser.add_argument(
          "--epochs", type=int, required=True, help="number of epochs"
      )
      parser.add_argument(
          "--dropout",
          type=float,
          required=True,
          help="dropout probability",
      )
      parser.add_argument(
          "--batch_size", type=int, required=True, help="batch size"
      )
      return parser.parse_args()
  
imports: import argparse
  import logging
  import os
  import time
  import warnings
  import torch
  from pytorch_lightning import LightningModule, loggers, Trainer
  from torch import nn
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torchmetrics.functional.classification.accuracy import multiclass_accuracy
  from torchvision import transforms
  from torchvision.datasets import MNIST
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser