class_docstr: The random crop and resize augmentation proposed in
  Sec. 6 of "Going Deeper with Convolutions" by Google.
  This implementation follows the details in ``fb.resnet.torch``.
  
  It attempts to crop a random rectangle with 8%~100% area of the original image,
  and keep the aspect ratio between 3/4 to 4/3. Then it resize this crop to the target shape.
  If such crop cannot be found in 10 iterations, it will do a ResizeShortestEdge + CenterCrop.
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docstr: Args:
      crop_area_fraction (tuple(float)): Defaults to crop 8%-100% area.
      aspect_ratio_range (tuple(float)): Defaults to make aspect ratio in 3/4-4/3.
      target_shape (int): Defaults to 224, the standard ImageNet image shape.
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code: def __init__(
      self,
      crop_area_fraction=(0.08, 1.0),
      aspect_ratio_range=(0.75, 1.333),
      target_shape=224,
      interp=cv2.INTER_LINEAR,
  ):
      """
      Args:
          crop_area_fraction (tuple(float)): Defaults to crop 8%-100% area.
          aspect_ratio_range (tuple(float)): Defaults to make aspect ratio in 3/4-4/3.
          target_shape (int): Defaults to 224, the standard ImageNet image shape.
      """
      super(GoogleNetRandomCropAndResize, self).__init__()
      self._init(locals())
  
imports: import numpy
  import cv2
  from ...utils.argtools import shape2d
  from ...utils.develop import log_deprecated
  from .base import ImageAugmentor, ImagePlaceholder
  from .transform import CropTransform, TransformList, ResizeTransform, PhotometricTransform
  from .misc import ResizeShortestEdge
label_desc: Use image node_id 1 to create a new GoogleNetRandomCropAndResize object from inherited base classes: ImageAugmentor with example: obj = GoogleNetRandomCropAndResize(crop_area_fraction, aspect_ratio_range, target_shape, interp)