code: def transforms_noaug_train(
      img_size: Union[int, Tuple[int, int]] = 224,
      interpolation: str = "bilinear",
      use_prefetcher: bool = False,
      mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
      std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
  ):
      """No-augmentation image transforms for training.
  
      Args:
          img_size: Target image size.
          interpolation: Image interpolation mode.
          mean: Image normalization mean.
          std: Image normalization standard deviation.
          use_prefetcher: Prefetcher enabled. Do not convert image to tensor or normalize.
  
      Returns:
  
      """
      if interpolation == "random":
          # random interpolation not supported with no-aug
          interpolation = "bilinear"
      tfl = [
          transforms.Resize(
              img_size, interpolation=str_to_interp_mode(interpolation)
          ),
          transforms.CenterCrop(img_size),
      ]
      if use_prefetcher:
          # prefetcher and collate will handle tensor conversion and norm
          tfl += [ToNumpy()]
      else:
          tfl += [
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=torch.tensor(mean), std=torch.tensor(std)
              ),
          ]
      return transforms.Compose(tfl)
  
file_path: pytorch-image-models/timm/data/transforms_factory.py
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from torchvision import transforms
  from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.data.auto_augment import rand_augment_transform, augment_and_mix_transform, auto_augment_transform
  from timm.data.transforms import str_to_interp_mode, str_to_pil_interp, RandomResizedCropAndInterpolation, ResizeKeepRatio, CenterCropOrPad, RandomCropOrPad, TrimBorder, ToNumpy
  from timm.data.random_erasing import RandomErasing
label_desc: Use image node_id 1 for calling a global function with example usage: transforms_noaug_train(img_size, interpolation, use_prefetcher, mean, std) and returns: transforms