code: def parse_args():
      """parse arguments"""
      parser = argparse.ArgumentParser()
      parser.add_argument("--input", type=str, required=True)
      parser.add_argument(
          "--learning_rate", type=float, default=0.005, required=False
      )
      parser.add_argument(
          "--batch_size", type=int, default=8, required=False
      )
      parser.add_argument(
          "--epochs", type=int, default=10, required=False
      )
      parser.add_argument("--output", type=str, default=".")
      parser.add_argument(
          "--accumulation_steps", type=int, default=1, required=False
      )
      return parser.parse_args()
  
imports: import argparse
  import ast
  import os
  import albumentations
  import numpy
  import pandas
  import timm
  import torch
  import torch.nn
  import torchvision
  from effdet import DetBenchTrain, EfficientDet, get_efficientdet_config
  from effdet.config.model_config import efficientdet_model_param_dict
  from effdet.efficientdet import HeadNet
  from PIL import Image, ImageFile
  from sklearn.model_selection import train_test_split
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
  from tez import Tez, TezConfig
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser