code: def __init__(self):
      fillcolor = (128, 128, 128)
      self.ranges = {
          "shearX": np.linspace(0, 0.3, 11),
          "shearY": np.linspace(0, 0.3, 11),
          "translateX": np.linspace(0, 250, 11),
          "translateY": np.linspace(0, 250, 11),
          "rotate": np.linspace(0, 30, 11),
          "color": np.linspace(0.1, 1.9, 11),
          "posterize": np.round(np.linspace(0, 4, 11), 0).astype(
              np.int
          ),
          "solarize": np.linspace(0, 256, 11),
          "solarizeadd": np.linspace(0, 110, 11),
          "contrast": np.linspace(0.1, 1.9, 11),
          "sharpness": np.linspace(0.1, 1.9, 11),
          "brightness": np.linspace(0.1, 1.9, 11),
          "autocontrast": [0] * 10,
          "equalize": [0] * 10,
          "invert": [0] * 10,
      }
  
      def rotate_with_fill(img, magnitude):
          magnitude *= random.choice([-1, 1])
          rot = img.convert("RGBA").rotate(magnitude)
          return Image.composite(
              rot, Image.new("RGBA", rot.size, (128,) * 4), rot
          ).convert(img.mode)
  
      def solarize_add(image, addition=0, threshold=128):
          lut = []
          for i in range(256):
              if i < threshold:
                  res = i + addition if i + addition <= 255 else 255
                  res = res if res >= 0 else 0
                  lut.append(res)
              else:
                  lut.append(i)
          from PIL.ImageOps import _lut
  
          return _lut(image, lut)
  
      self.operations = {
          "shearX": lambda img, magnitude: img.transform(
              img.size,
              Image.AFFINE,
              (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
              Image.BICUBIC,
              fillcolor=fillcolor,
          ),
          "shearY": lambda img, magnitude: img.transform(
              img.size,
              Image.AFFINE,
              (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
              Image.BICUBIC,
              fillcolor=fillcolor,
          ),
          "translateX": lambda img, magnitude: img.transform(
              img.size,
              Image.AFFINE,
              (1, 0, magnitude * random.choice([-1, 1]), 0, 1, 0),
              fillcolor=fillcolor,
          ),
          "translateY": lambda img, magnitude: img.transform(
              img.size,
              Image.AFFINE,
              (1, 0, 0, 0, 1, magnitude * random.choice([-1, 1])),
              fillcolor=fillcolor,
          ),
          "rotate": lambda img, magnitude: rotate_with_fill(
              img, magnitude
          ),
          "color": lambda img, magnitude: ImageEnhance.Color(
              img
          ).enhance(magnitude),
          "posterize": lambda img, magnitude: ImageOps.posterize(
              img, magnitude
          ),
          "solarize": lambda img, magnitude: ImageOps.solarize(
              img, magnitude
          ),
          "solarizeadd": lambda img, magnitude: solarize_add(
              img, magnitude
          ),
          "contrast": lambda img, magnitude: ImageEnhance.Contrast(
              img
          ).enhance(magnitude),
          "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(
              img
          ).enhance(magnitude),
          "brightness": lambda img, magnitude: ImageEnhance.Brightness(
              img
          ).enhance(magnitude),
          "autocontrast": lambda img, _: ImageOps.autocontrast(img),
          "equalize": lambda img, _: ImageOps.equalize(img),
          "invert": lambda img, _: ImageOps.invert(img),
      }
  
file_path: DALI/docs/examples/use_cases/pytorch/efficientnet/image_classification/autoaugment.py
imports: from PIL import Image, ImageEnhance, ImageOps
  import numpy
  import random
label_desc: Use image node_id 1 to create a new OperationFactory object  with example: obj = OperationFactory()