code: def build_train_transform(self, distort_color, resize_scale):
      print("Color jitter: %s" % distort_color)
      if distort_color == "strong":
          color_transform = transforms.ColorJitter(
              brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1
          )
      elif distort_color == "normal":
          color_transform = transforms.ColorJitter(
              brightness=32.0 / 255.0, saturation=0.5
          )
      else:
          color_transform = None
      if color_transform is None:
          train_transforms = transforms.Compose(
              [
                  transforms.RandomResizedCrop(
                      self.image_size, scale=(resize_scale, 1.0)
                  ),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  self.normalize,
              ]
          )
      else:
          train_transforms = transforms.Compose(
              [
                  transforms.RandomResizedCrop(
                      self.image_size, scale=(resize_scale, 1.0)
                  ),
                  transforms.RandomHorizontalFlip(),
                  color_transform,
                  transforms.ToTensor(),
                  self.normalize,
              ]
          )
      return train_transforms
  
imports: import os
  import numpy
  import torch.utils.data
  import torchvision.transforms
  import torchvision.datasets
label_desc: Use image node_id 10 for calling the ImagenetDataProvider obj's underlying member method code with example usage: obj.build_train_transform(distort_color, resize_scale) and returns: train_transforms