code: def _transforms(dataset):
      transforms = []
      if dataset == "human":
          transforms.append(
              T.PaddingByAspectRatio(aspect_ratio=1.77777778)
          )
          transforms.append(T.Resize(target_size=[398, 224]))
          transforms.append(T.Normalize())
      elif dataset == "cityscapes" or dataset == "ade":
          transforms.append(T.Normalize())
      return transforms
  
file_path: PaddleSeg/deploy/slim/act/test_seg.py
imports: import argparse
  import time
  import os
  import sys
  import cv2
  import numpy
  import paddle
  import paddleseg.transforms
  from paddleseg.cvlibs import Config
  from paddleseg.core.infer import reverse_transform
  from paddleseg.utils.visualize import get_pseudo_color_map
  from paddleseg.utils import metrics
  from paddleseg.cvlibs import SegBuilder
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
label_desc: Use image node_id 1 for calling a global function with example usage: _transforms(dataset) and returns: transforms