code: def get_transform(
      train, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
  ):
      base_size = 565
      crop_size = 480
  
      if train:
          return SegmentationPresetTrain(
              base_size, crop_size, mean=mean, std=std
          )
      else:
          return SegmentationPresetEval(mean=mean, std=std)
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/unet/train_multi_GPU.py
imports: import time
  import os
  import datetime
  import torch
  from src import UNet
  from train_utils import train_one_epoch, evaluate, create_lr_scheduler, init_distributed_mode, save_on_master, mkdir
  from my_dataset import DriveDataset
  import transforms
label_desc: Use image node_id 1 for calling a global function with example usage: get_transform(train, mean, std) and returns: SegmentationPresetTrain, SegmentationPresetEval