code: def _apply_image_or_video_transform(
      self,
      image: ImageOrVideo,
      transform_id: str,
      magnitude: float,
      interpolation: Union[InterpolationMode, int],
      fill: Dict[Union[Type, str], _FillTypeJIT],
  ) -> ImageOrVideo:
      fill_ = _get_fill(fill, type(image))
  
      if transform_id == "Identity":
          return image
      elif transform_id == "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
          return F.affine(
              image,
              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 transform_id == "ShearY":
          # magnitude should be arctan(magnitude)
          # See above
          return F.affine(
              image,
              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 transform_id == "TranslateX":
          return F.affine(
              image,
              angle=0.0,
              translate=[int(magnitude), 0],
              scale=1.0,
              interpolation=interpolation,
              shear=[0.0, 0.0],
              fill=fill_,
          )
      elif transform_id == "TranslateY":
          return F.affine(
              image,
              angle=0.0,
              translate=[0, int(magnitude)],
              scale=1.0,
              interpolation=interpolation,
              shear=[0.0, 0.0],
              fill=fill_,
          )
      elif transform_id == "Rotate":
          return F.rotate(
              image,
              angle=magnitude,
              interpolation=interpolation,
              fill=fill_,
          )
      elif transform_id == "Brightness":
          return F.adjust_brightness(
              image, brightness_factor=1.0 + magnitude
          )
      elif transform_id == "Color":
          return F.adjust_saturation(
              image, saturation_factor=1.0 + magnitude
          )
      elif transform_id == "Contrast":
          return F.adjust_contrast(
              image, contrast_factor=1.0 + magnitude
          )
      elif transform_id == "Sharpness":
          return F.adjust_sharpness(
              image, sharpness_factor=1.0 + magnitude
          )
      elif transform_id == "Posterize":
          return F.posterize(image, bits=int(magnitude))
      elif transform_id == "Solarize":
          bound = (
              _FT._max_value(image.dtype)
              if isinstance(image, torch.Tensor)
              else 255.0
          )
          return F.solarize(image, threshold=bound * magnitude)
      elif transform_id == "AutoContrast":
          return F.autocontrast(image)
      elif transform_id == "Equalize":
          return F.equalize(image)
      elif transform_id == "Invert":
          return F.invert(image)
      else:
          raise ValueError(f"No transform available for {transform_id}")
  
imports: import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
  from torchvision import transforms, tv_tensors
  from torchvision.transforms import _functional_tensor
  from torchvision.transforms.v2 import AutoAugmentPolicy, functional, InterpolationMode, Transform
  from torchvision.transforms.v2.functional._geometry import _check_interpolation
  from torchvision.transforms.v2.functional._meta import get_size
  from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
  from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
label_desc: Use image node_id 6 for calling the _AutoAugmentBase obj's underlying member method code with example usage: obj._apply_image_or_video_transform(image, transform_id, magnitude, interpolation, fill) and returns: image, F, F, F, F, F, F, F, F, F, F, F, F, F, F