class_docstr: The default image transform with data augmentation.
  
  It is often useful for training models on Imagenet. It sequentially resizes
  the image into a random scale, takes a random spatial cropping, randomly flips
  the image horizontally, transforms PIL image data into a torch.Tensor and
  normalizes the pixel values by mean subtraction and standard deviation division.
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docstr: The constructor method of ImagenetAugmentTransform class.
  
  Args:
      crop_size: expected output size per dimension after random cropping
      mean: a 3-tuple denoting the pixel RGB mean
      std: a 3-tuple denoting the pixel RGB standard deviation
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code: def __init__(
      self,
      crop_size: int = ImagenetConstants.CROP_SIZE,
      mean: List[float] = ImagenetConstants.MEAN,
      std: List[float] = ImagenetConstants.STD,
  ):
      """The constructor method of ImagenetAugmentTransform class.
  
      Args:
          crop_size: expected output size per dimension after random cropping
          mean: a 3-tuple denoting the pixel RGB mean
          std: a 3-tuple denoting the pixel RGB standard deviation
  
      """
      self.transform = transforms.Compose(
          [
              transforms.RandomResizedCrop(crop_size),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              transforms.Normalize(mean=mean, std=std),
          ]
      )
  
imports: import collections
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torchvision.transforms
  from .None import build_transforms, ClassyTransform, register_transform
label_desc: Use image node_id 1 to create a new ImagenetAugmentTransform object from inherited base classes: ClassyTransform with example: obj = ImagenetAugmentTransform(crop_size, mean, std)