code: def _apply_op(
      img: Tensor,
      op_name: str,
      magnitude: float,
      interpolation: InterpolationMode,
      fill: Optional[List[float]],
  ):
      if op_name == "ShearX":
          # magnitude should be arctan(magnitude)
          # official autoaug: (1, level, 0, 0, 1, 0)
          # https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290
          # compared to
          # torchvision:      (1, tan(level), 0, 0, 1, 0)
          # https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976
          img = F.affine(
              img,
              angle=0.0,
              translate=[0, 0],
              scale=1.0,
              shear=[math.degrees(math.atan(magnitude)), 0.0],
              interpolation=interpolation,
              fill=fill,
              center=[0, 0],
          )
      elif op_name == "ShearY":
          # magnitude should be arctan(magnitude)
          # See above
          img = F.affine(
              img,
              angle=0.0,
              translate=[0, 0],
              scale=1.0,
              shear=[0.0, math.degrees(math.atan(magnitude))],
              interpolation=interpolation,
              fill=fill,
              center=[0, 0],
          )
      elif op_name == "TranslateX":
          img = F.affine(
              img,
              angle=0.0,
              translate=[int(magnitude), 0],
              scale=1.0,
              interpolation=interpolation,
              shear=[0.0, 0.0],
              fill=fill,
          )
      elif op_name == "TranslateY":
          img = F.affine(
              img,
              angle=0.0,
              translate=[0, int(magnitude)],
              scale=1.0,
              interpolation=interpolation,
              shear=[0.0, 0.0],
              fill=fill,
          )
      elif op_name == "Rotate":
          img = F.rotate(
              img, magnitude, interpolation=interpolation, fill=fill
          )
      elif op_name == "Brightness":
          img = F.adjust_brightness(img, 1.0 + magnitude)
      elif op_name == "Color":
          img = F.adjust_saturation(img, 1.0 + magnitude)
      elif op_name == "Contrast":
          img = F.adjust_contrast(img, 1.0 + magnitude)
      elif op_name == "Sharpness":
          img = F.adjust_sharpness(img, 1.0 + magnitude)
      elif op_name == "Posterize":
          img = F.posterize(img, int(magnitude))
      elif op_name == "Solarize":
          img = F.solarize(img, magnitude)
      elif op_name == "AutoContrast":
          img = F.autocontrast(img)
      elif op_name == "Equalize":
          img = F.equalize(img)
      elif op_name == "Invert":
          img = F.invert(img)
      elif op_name == "Identity":
          pass
      else:
          raise ValueError(
              f"The provided operator {op_name} is not recognized."
          )
      return img
  
imports: import math
  from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import Tensor
  from .None import functional, InterpolationMode
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_op(img, op_name, magnitude, interpolation, fill) and returns: img