code: def get_transform(is_train, args):
      if is_train:
          return presets.SegmentationPresetTrain(
              base_size=520,
              crop_size=480,
              backend=args.backend,
              use_v2=args.use_v2,
          )
      elif args.weights and args.test_only:
          weights = torchvision.models.get_weight(args.weights)
          trans = weights.transforms()
  
          def preprocessing(img, target):
              img = trans(img)
              size = F.get_dimensions(img)[1:]
              target = F.resize(
                  target, size, interpolation=InterpolationMode.NEAREST
              )
              return img, F.pil_to_tensor(target)
  
          return preprocessing
      else:
          return presets.SegmentationPresetEval(
              base_size=520, backend=args.backend, use_v2=args.use_v2
          )
  
file_path: vision/references/segmentation/train.py
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import utils
  from coco_utils import get_coco
  from torch import nn
  from torch.optim.lr_scheduler import PolynomialLR
  from torchvision.transforms import functional, InterpolationMode
label_desc: Use image node_id 2 for calling a global function with example usage: get_transform(is_train, args) and returns: presets, preprocessing, presets, img,  F