code: def transforms_imagenet_train(
      img_size=224,
      scale=None,
      ratio=None,
      hflip=0.5,
      vflip=0.0,
      color_jitter=0.4,
      auto_augment=None,
      interpolation="random",
      use_prefetcher=False,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
      re_prob=0.0,
      re_mode="const",
      re_count=1,
      re_num_splits=0,
      separate=False,
  ):
      """
      If separate==True, the transforms are returned as a tuple of 3 separate transforms
      for use in a mixing dataset that passes
       * all data through the first (primary) transform, called the 'clean' data
       * a portion of the data through the secondary transform
       * normalizes and converts the branches above with the third, final transform
      """
      if isinstance(img_size, tuple):
          img_size = img_size[-2:]
      else:
          img_size = img_size
  
      scale = tuple(
          scale or (0.08, 1.0)
      )  # default imagenet scale range
      ratio = tuple(
          ratio or (3.0 / 4.0, 4.0 / 3.0)
      )  # default imagenet ratio range
      primary_tfl = [
          RandomResizedCropAndInterpolation(
              img_size,
              scale=scale,
              ratio=ratio,
              interpolation=interpolation,
          )
      ]
      if hflip > 0.0:
          primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)]
      if vflip > 0.0:
          primary_tfl += [transforms.RandomVerticalFlip(p=vflip)]
  
      secondary_tfl = []
      if auto_augment:
          assert isinstance(auto_augment, str)
          if isinstance(img_size, tuple):
              img_size_min = min(img_size)
          else:
              img_size_min = img_size
          aa_params = dict(
              translate_const=int(img_size_min * 0.45),
              img_mean=tuple([min(255, round(255 * x)) for x in mean]),
          )
          if interpolation and interpolation != "random":
              aa_params["interpolation"] = _pil_interp(interpolation)
          if auto_augment.startswith("rand"):
              secondary_tfl += [
                  rand_augment_transform(auto_augment, aa_params)
              ]
          elif auto_augment.startswith("augmix"):
              raise NotImplementedError("Augmix not implemented")
          else:
              raise NotImplementedError("Auto aug not implemented")
      elif color_jitter is not None:
          # color jitter is enabled when not using AA
          if isinstance(color_jitter, (list, tuple)):
              # color jitter should be a 3-tuple/list if spec brightness/contrast/saturation
              # or 4 if also augmenting hue
              assert len(color_jitter) in (3, 4)
          else:
              # if it's a scalar, duplicate for brightness, contrast, and saturation, no hue
              color_jitter = (float(color_jitter),) * 3
          secondary_tfl += [transforms.ColorJitter(*color_jitter)]
  
      final_tfl = []
      final_tfl += [
          transforms.ToTensor(),
          transforms.Normalize(
              mean=torch.tensor(mean), std=torch.tensor(std)
          ),
      ]
      if re_prob > 0.0:
          final_tfl.append(
              RandomErasing(
                  re_prob,
                  mode=re_mode,
                  max_count=re_count,
                  num_splits=re_num_splits,
                  device="cpu",
                  cube=False,
              )
          )
  
      if separate:
          return (
              transforms.Compose(primary_tfl),
              transforms.Compose(secondary_tfl),
              transforms.Compose(final_tfl),
          )
      else:
          return transforms.Compose(
              primary_tfl + secondary_tfl + final_tfl
          )
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 21 for calling a global function with example usage: transforms_imagenet_train(img_size, scale, ratio, hflip, vflip, color_jitter, auto_augment, interpolation, use_prefetcher, mean, std, re_prob, re_mode, re_count, re_num_splits, separate) and returns: transforms,  transforms,  transforms, transforms