code: def get_transforms_eval(model_name, img_size=224, crop_pct=None):
      crop_pct = crop_pct or DEFAULT_CROP_PCT
      if "dpn" in model_name:
          if crop_pct is None:
              # Use default 87.5% crop for model's native img_size
              # but use 100% crop for larger than native as it
              # improves test time results across all models.
              if img_size == 224:
                  scale_size = int(
                      math.floor(img_size / DEFAULT_CROP_PCT)
                  )
              else:
                  scale_size = img_size
          else:
              scale_size = int(math.floor(img_size / crop_pct))
          normalize = transforms.Normalize(
              mean=[124 / 255, 117 / 255, 104 / 255],
              std=[1 / (0.0167 * 255)] * 3,
          )
      elif "inception" in model_name:
          scale_size = int(math.floor(img_size / crop_pct))
          normalize = LeNormalize()
      else:
          scale_size = int(math.floor(img_size / crop_pct))
          normalize = transforms.Normalize(
              mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
          )
  
      return transforms.Compose(
          [
              transforms.Scale(scale_size, Image.BICUBIC),
              transforms.CenterCrop(img_size),
              transforms.ToTensor(),
              normalize,
          ]
      )
  
file_path: pywick/pywick/models/classification/dpn/model_factory.py
imports: import math
  from .dualpath import *
  from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
  from torchvision.models.densenet import densenet121, densenet169, densenet161, densenet201
  from torchvision.models.inception import inception_v3
  import torchvision.transforms
  from PIL import Image
label_desc: Use image node_id 2 for calling a global function with example usage: get_transforms_eval(model_name, img_size, crop_pct) and returns: transforms