code: def get_dataset(args, is_train):
      def sbd(*args, **kwargs):
          kwargs.pop("use_v2")
          return torchvision.datasets.SBDataset(
              *args, mode="segmentation", **kwargs
          )
  
      def voc(*args, **kwargs):
          kwargs.pop("use_v2")
          return torchvision.datasets.VOCSegmentation(*args, **kwargs)
  
      paths = {
          "voc": (args.data_path, voc, 21),
          "voc_aug": (args.data_path, sbd, 21),
          "coco": (args.data_path, get_coco, 21),
      }
      p, ds_fn, num_classes = paths[args.dataset]
  
      image_set = "train" if is_train else "val"
      ds = ds_fn(
          p,
          image_set=image_set,
          transforms=get_transform(is_train, args),
          use_v2=args.use_v2,
      )
      return ds, num_classes
  
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 1 for calling a global function with example usage: get_dataset(args, is_train) and returns: ds,  num_classes, torchvision, torchvision