text: import math
  import numbers
  import warnings
  from enum import Enum
  from typing import List, Tuple, Any, Optional
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import Tensor
  
  try:
      import accimage
  except ImportError:
      accimage = None
  
  from . import functional_pil as F_pil
  from . import functional_tensor as F_t
  
  
  class InterpolationMode(Enum):
      """Interpolation modes
      Available interpolation methods are ``nearest``, ``bilinear``, ``bicubic``, ``box``, ``hamming``, and ``lanczos``.
      """
  
      NEAREST = "nearest"
      BILINEAR = "bilinear"
      BICUBIC = "bicubic"
      # For PIL compatibility
      BOX = "box"
      HAMMING = "hamming"
      LANCZOS = "lanczos"
  
  
  # TODO: Once torchscript supports Enums with staticmethod
  # this can be put into InterpolationMode as staticmethod
  def _interpolation_modes_from_int(i: int) -> InterpolationMode:
      inverse_modes_mapping = {
          0: InterpolationMode.NEAREST,
          2: InterpolationMode.BILINEAR,
          3: InterpolationMode.BICUBIC,
          4: InterpolationMode.BOX,
          5: InterpolationMode.HAMMING,
          1: InterpolationMode.LANCZOS,
      }
      return inverse_modes_mapping[i]
  
  
  pil_modes_mapping = {
      InterpolationMode.NEAREST: 0,
      InterpolationMode.BILINEAR: 2,
      InterpolationMode.BICUBIC: 3,
      InterpolationMode.BOX: 4,
      InterpolationMode.HAMMING: 5,
      InterpolationMode.LANCZOS: 1,
  }
  
  _is_pil_image = F_pil._is_pil_image
  
  
  def get_image_size(img: Tensor) -> List[int]:
      """Returns the size of an image as [width, height].
  
      Args:
          img (PIL Image or Tensor): The image to be checked.
  
      Returns:
          List[int]: The image size.
      """
      if isinstance(img, torch.Tensor):
          return F_t.get_image_size(img)
  
      return F_pil.get_image_size(img)
  
  
  def get_image_num_channels(img: Tensor) -> int:
      """Returns the number of channels of an image.
  
      Args:
          img (PIL Image or Tensor): The image to be checked.
  
      Returns:
          int: The number of channels.
      """
      if isinstance(img, torch.Tensor):
          return F_t.get_image_num_channels(img)
  
      return F_pil.get_image_num_channels(img)
  
  
  @torch.jit.unused
  def _is_numpy(img: Any) -> bool:
      return isinstance(img, np.ndarray)
  
  
  @torch.jit.unused
  def _is_numpy_image(img: Any) -> bool:
      return img.ndim in {2, 3}
  
  
  def to_tensor(pic):
      """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
      This function does not support torchscript.
  
      See :class:`~torchvision.transforms.ToTensor` for more details.
  
      Args:
          pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
  
      Returns:
          Tensor: Converted image.
      """
      if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
          raise TypeError("pic should be PIL Image or ndarray. Got {}".format(type(pic)))
  
      if _is_numpy(pic) and not _is_numpy_image(pic):
          raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim))
  
      default_float_dtype = torch.get_default_dtype()
  
      if isinstance(pic, np.ndarray):
          # handle numpy array
          if pic.ndim == 2:
              pic = pic[:, :, None]
  
          img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
          # backward compatibility
          if isinstance(img, torch.ByteTensor):
              return img.to(dtype=default_float_dtype).div(255)
          else:
              return img
  
      if accimage is not None and isinstance(pic, accimage.Image):
          nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
          pic.copyto(nppic)
          return torch.from_numpy(nppic).to(dtype=default_float_dtype)
  
      # handle PIL Image
      mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32}
      img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))
  
      if pic.mode == "1":
          img = 255 * img
      img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
      # put it from HWC to CHW format
      img = img.permute((2, 0, 1)).contiguous()
      if isinstance(img, torch.ByteTensor):
          return img.to(dtype=default_float_dtype).div(255)
      else:
          return img
  
  
  def pil_to_tensor(pic):
      """Convert a ``PIL Image`` to a tensor of the same type.
      This function does not support torchscript.
  
      See :class:`~torchvision.transforms.PILToTensor` for more details.
  
      Args:
          pic (PIL Image): Image to be converted to tensor.
  
      Returns:
          Tensor: Converted image.
      """
      if not F_pil._is_pil_image(pic):
          raise TypeError("pic should be PIL Image. Got {}".format(type(pic)))
  
      if accimage is not None and isinstance(pic, accimage.Image):
          # accimage format is always uint8 internally, so always return uint8 here
          nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.uint8)
          pic.copyto(nppic)
          return torch.as_tensor(nppic)
  
      # handle PIL Image
      img = torch.as_tensor(np.asarray(pic))
      img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
      # put it from HWC to CHW format
      img = img.permute((2, 0, 1))
      return img
  
  
  def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
      """Convert a tensor image to the given ``dtype`` and scale the values accordingly
      This function does not support PIL Image.
  
      Args:
          image (torch.Tensor): Image to be converted
          dtype (torch.dtype): Desired data type of the output
  
      Returns:
          Tensor: Converted image
  
      .. note::
  
          When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
          If converted back and forth, this mismatch has no effect.
  
      Raises:
          RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as
              well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to
              overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
              of the integer ``dtype``.
      """
      if not isinstance(image, torch.Tensor):
          raise TypeError("Input img should be Tensor Image")
  
      return F_t.convert_image_dtype(image, dtype)
  
  
  def to_pil_image(pic, mode=None):
      """Convert a tensor or an ndarray to PIL Image. This function does not support torchscript.
  
      See :class:`~torchvision.transforms.ToPILImage` for more details.
  
      Args:
          pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
          mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
  
      .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes
  
      Returns:
          PIL Image: Image converted to PIL Image.
      """
      if not (isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
          raise TypeError("pic should be Tensor or ndarray. Got {}.".format(type(pic)))
  
      elif isinstance(pic, torch.Tensor):
          if pic.ndimension() not in {2, 3}:
              raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndimension()))
  
          elif pic.ndimension() == 2:
              # if 2D image, add channel dimension (CHW)
              pic = pic.unsqueeze(0)
  
          # check number of channels
          if pic.shape[-3] > 4:
              raise ValueError("pic should not have > 4 channels. Got {} channels.".format(pic.shape[-3]))
  
      elif isinstance(pic, np.ndarray):
          if pic.ndim not in {2, 3}:
              raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim))
  
          elif pic.ndim == 2:
              # if 2D image, add channel dimension (HWC)
              pic = np.expand_dims(pic, 2)
  
          # check number of channels
          if pic.shape[-1] > 4:
              raise ValueError("pic should not have > 4 channels. Got {} channels.".format(pic.shape[-1]))
  
      npimg = pic
      if isinstance(pic, torch.Tensor):
          if pic.is_floating_point() and mode != "F":
              pic = pic.mul(255).byte()
          npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0))
  
      if not isinstance(npimg, np.ndarray):
          raise TypeError("Input pic must be a torch.Tensor or NumPy ndarray, " + "not {}".format(type(npimg)))
  
      if npimg.shape[2] == 1:
          expected_mode = None
          npimg = npimg[:, :, 0]
          if npimg.dtype == np.uint8:
              expected_mode = "L"
          elif npimg.dtype == np.int16:
              expected_mode = "I;16"
          elif npimg.dtype == np.int32:
              expected_mode = "I"
          elif npimg.dtype == np.float32:
              expected_mode = "F"
          if mode is not None and mode != expected_mode:
              raise ValueError(
                  "Incorrect mode ({}) supplied for input type {}. Should be {}".format(mode, np.dtype, expected_mode)
              )
          mode = expected_mode
  
      elif npimg.shape[2] == 2:
          permitted_2_channel_modes = ["LA"]
          if mode is not None and mode not in permitted_2_channel_modes:
              raise ValueError("Only modes {} are supported for 2D inputs".format(permitted_2_channel_modes))
  
          if mode is None and npimg.dtype == np.uint8:
              mode = "LA"
  
      elif npimg.shape[2] == 4:
          permitted_4_channel_modes = ["RGBA", "CMYK", "RGBX"]
          if mode is not None and mode not in permitted_4_channel_modes:
              raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))
  
          if mode is None and npimg.dtype == np.uint8:
              mode = "RGBA"
      else:
          permitted_3_channel_modes = ["RGB", "YCbCr", "HSV"]
          if mode is not None and mode not in permitted_3_channel_modes:
              raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
          if mode is None and npimg.dtype == np.uint8:
              mode = "RGB"
  
      if mode is None:
          raise TypeError("Input type {} is not supported".format(npimg.dtype))
  
      return Image.fromarray(npimg, mode=mode)
  
  
  def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor:
      """Normalize a float tensor image with mean and standard deviation.
      This transform does not support PIL Image.
  
      .. note::
          This transform acts out of place by default, i.e., it does not mutates the input tensor.
  
      See :class:`~torchvision.transforms.Normalize` for more details.
  
      Args:
          tensor (Tensor): Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized.
          mean (sequence): Sequence of means for each channel.
          std (sequence): Sequence of standard deviations for each channel.
          inplace(bool,optional): Bool to make this operation inplace.
  
      Returns:
          Tensor: Normalized Tensor image.
      """
      if not isinstance(tensor, torch.Tensor):
          raise TypeError("Input tensor should be a torch tensor. Got {}.".format(type(tensor)))
  
      if not tensor.is_floating_point():
          raise TypeError("Input tensor should be a float tensor. Got {}.".format(tensor.dtype))
  
      if tensor.ndim < 3:
          raise ValueError(
              "Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = "
              "{}.".format(tensor.size())
          )
  
      if not inplace:
          tensor = tensor.clone()
  
      dtype = tensor.dtype
      mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
      std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
      if (std == 0).any():
          raise ValueError("std evaluated to zero after conversion to {}, leading to division by zero.".format(dtype))
      if mean.ndim == 1:
          mean = mean.view(-1, 1, 1)
      if std.ndim == 1:
          std = std.view(-1, 1, 1)
      tensor.sub_(mean).div_(std)
      return tensor
  
  
  def resize(
      img: Tensor,
      size: List[int],
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
      max_size: Optional[int] = None,
      antialias: Optional[bool] = None,
  ) -> Tensor:
      r"""Resize the input image to the given size.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      .. warning::
          The output image might be different depending on its type: when downsampling, the interpolation of PIL images
          and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
          in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
          types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
          closer.
  
      Args:
          img (PIL Image or Tensor): Image to be resized.
          size (sequence or int): Desired output size. If size is a sequence like
              (h, w), the output size will be matched to this. If size is an int,
              the smaller edge of the image will be matched to this number maintaining
              the aspect ratio. i.e, if height > width, then image will be rescaled to
              :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
  
              .. note::
                  In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`.
              Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
              ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
              For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
          max_size (int, optional): The maximum allowed for the longer edge of
              the resized image: if the longer edge of the image is greater
              than ``max_size`` after being resized according to ``size``, then
              the image is resized again so that the longer edge is equal to
              ``max_size``. As a result, ``size`` might be overruled, i.e the
              smaller edge may be shorter than ``size``. This is only supported
              if ``size`` is an int (or a sequence of length 1 in torchscript
              mode).
          antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
              is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
              ``InterpolationMode.BILINEAR`` only mode. This can help making the output for PIL images and tensors
              closer.
  
              .. warning::
                  There is no autodiff support for ``antialias=True`` option with input ``img`` as Tensor.
  
      Returns:
          PIL Image or Tensor: Resized image.
      """
      # Backward compatibility with integer value
      if isinstance(interpolation, int):
          warnings.warn(
              "Argument interpolation should be of type InterpolationMode instead of int. "
              "Please, use InterpolationMode enum."
          )
          interpolation = _interpolation_modes_from_int(interpolation)
  
      if not isinstance(interpolation, InterpolationMode):
          raise TypeError("Argument interpolation should be a InterpolationMode")
  
      if not isinstance(img, torch.Tensor):
          if antialias is not None and not antialias:
              warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
          pil_interpolation = pil_modes_mapping[interpolation]
          return F_pil.resize(img, size=size, interpolation=pil_interpolation, max_size=max_size)
  
      return F_t.resize(img, size=size, interpolation=interpolation.value, max_size=max_size, antialias=antialias)
  
  
  def scale(*args, **kwargs):
      warnings.warn("The use of the transforms.Scale transform is deprecated, " + "please use transforms.Resize instead.")
      return resize(*args, **kwargs)
  
  
  def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
      r"""Pad the given image on all sides with the given "pad" value.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
      at most 3 leading dimensions for mode edge,
      and an arbitrary number of leading dimensions for mode constant
  
      Args:
          img (PIL Image or Tensor): Image to be padded.
          padding (int or sequence): Padding on each border. If a single int is provided this
              is used to pad all borders. If sequence of length 2 is provided this is the padding
              on left/right and top/bottom respectively. If a sequence of length 4 is provided
              this is the padding for the left, top, right and bottom borders respectively.
  
              .. note::
                  In torchscript mode padding as single int is not supported, use a sequence of
                  length 1: ``[padding, ]``.
          fill (number or str or tuple): Pixel fill value for constant fill. Default is 0.
              If a tuple of length 3, it is used to fill R, G, B channels respectively.
              This value is only used when the padding_mode is constant.
              Only number is supported for torch Tensor.
              Only int or str or tuple value is supported for PIL Image.
          padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
              Default is constant.
  
              - constant: pads with a constant value, this value is specified with fill
  
              - edge: pads with the last value at the edge of the image.
                If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
  
              - reflect: pads with reflection of image without repeating the last value on the edge.
                For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
                will result in [3, 2, 1, 2, 3, 4, 3, 2]
  
              - symmetric: pads with reflection of image repeating the last value on the edge.
                For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
                will result in [2, 1, 1, 2, 3, 4, 4, 3]
  
      Returns:
          PIL Image or Tensor: Padded image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
  
      return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
  
  
  def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
      """Crop the given image at specified location and output size.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
      If image size is smaller than output size along any edge, image is padded with 0 and then cropped.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
          top (int): Vertical component of the top left corner of the crop box.
          left (int): Horizontal component of the top left corner of the crop box.
          height (int): Height of the crop box.
          width (int): Width of the crop box.
  
      Returns:
          PIL Image or Tensor: Cropped image.
      """
  
      if not isinstance(img, torch.Tensor):
          return F_pil.crop(img, top, left, height, width)
  
      return F_t.crop(img, top, left, height, width)
  
  
  def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
      """Crops the given image at the center.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
      If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped.
          output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int,
              it is used for both directions.
  
      Returns:
          PIL Image or Tensor: Cropped image.
      """
      if isinstance(output_size, numbers.Number):
          output_size = (int(output_size), int(output_size))
      elif isinstance(output_size, (tuple, list)) and len(output_size) == 1:
          output_size = (output_size[0], output_size[0])
  
      image_width, image_height = get_image_size(img)
      crop_height, crop_width = output_size
  
      if crop_width > image_width or crop_height > image_height:
          padding_ltrb = [
              (crop_width - image_width) // 2 if crop_width > image_width else 0,
              (crop_height - image_height) // 2 if crop_height > image_height else 0,
              (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
              (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
          ]
          img = pad(img, padding_ltrb, fill=0)  # PIL uses fill value 0
          image_width, image_height = get_image_size(img)
          if crop_width == image_width and crop_height == image_height:
              return img
  
      crop_top = int(round((image_height - crop_height) / 2.0))
      crop_left = int(round((image_width - crop_width) / 2.0))
      return crop(img, crop_top, crop_left, crop_height, crop_width)
  
  
  def resized_crop(
      img: Tensor,
      top: int,
      left: int,
      height: int,
      width: int,
      size: List[int],
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
  ) -> Tensor:
      """Crop the given image and resize it to desired size.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
          top (int): Vertical component of the top left corner of the crop box.
          left (int): Horizontal component of the top left corner of the crop box.
          height (int): Height of the crop box.
          width (int): Width of the crop box.
          size (sequence or int): Desired output size. Same semantics as ``resize``.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`.
              Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
              ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
              For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
  
      Returns:
          PIL Image or Tensor: Cropped image.
      """
      img = crop(img, top, left, height, width)
      img = resize(img, size, interpolation)
      return img
  
  
  def hflip(img: Tensor) -> Tensor:
      """Horizontally flip the given image.
  
      Args:
          img (PIL Image or Tensor): Image to be flipped. If img
              is a Tensor, it is expected to be in [..., H, W] format,
              where ... means it can have an arbitrary number of leading
              dimensions.
  
      Returns:
          PIL Image or Tensor:  Horizontally flipped image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.hflip(img)
  
      return F_t.hflip(img)
  
  
  def _get_perspective_coeffs(startpoints: List[List[int]], endpoints: List[List[int]]) -> List[float]:
      """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
  
      In Perspective Transform each pixel (x, y) in the original image gets transformed as,
       (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) )
  
      Args:
          startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
              ``[top-left, top-right, bottom-right, bottom-left]`` of the original image.
          endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
              ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image.
  
      Returns:
          octuple (a, b, c, d, e, f, g, h) for transforming each pixel.
      """
      a_matrix = torch.zeros(2 * len(startpoints), 8, dtype=torch.float)
  
      for i, (p1, p2) in enumerate(zip(endpoints, startpoints)):
          a_matrix[2 * i, :] = torch.tensor([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]])
          a_matrix[2 * i + 1, :] = torch.tensor([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]])
  
      b_matrix = torch.tensor(startpoints, dtype=torch.float).view(8)
      res = torch.linalg.lstsq(a_matrix, b_matrix, driver="gels").solution
  
      output: List[float] = res.tolist()
      return output
  
  
  def perspective(
      img: Tensor,
      startpoints: List[List[int]],
      endpoints: List[List[int]],
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
      fill: Optional[List[float]] = None,
  ) -> Tensor:
      """Perform perspective transform of the given image.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
  
      Args:
          img (PIL Image or Tensor): Image to be transformed.
          startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
              ``[top-left, top-right, bottom-right, bottom-left]`` of the original image.
          endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
              ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
              If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
              For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
          fill (sequence or number, optional): Pixel fill value for the area outside the transformed
              image. If given a number, the value is used for all bands respectively.
  
              .. note::
                  In torchscript mode single int/float value is not supported, please use a sequence
                  of length 1: ``[value, ]``.
  
      Returns:
          PIL Image or Tensor: transformed Image.
      """
  
      coeffs = _get_perspective_coeffs(startpoints, endpoints)
  
      # Backward compatibility with integer value
      if isinstance(interpolation, int):
          warnings.warn(
              "Argument interpolation should be of type InterpolationMode instead of int. "
              "Please, use InterpolationMode enum."
          )
          interpolation = _interpolation_modes_from_int(interpolation)
  
      if not isinstance(interpolation, InterpolationMode):
          raise TypeError("Argument interpolation should be a InterpolationMode")
  
      if not isinstance(img, torch.Tensor):
          pil_interpolation = pil_modes_mapping[interpolation]
          return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill)
  
      return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill)
  
  
  def vflip(img: Tensor) -> Tensor:
      """Vertically flip the given image.
  
      Args:
          img (PIL Image or Tensor): Image to be flipped. If img
              is a Tensor, it is expected to be in [..., H, W] format,
              where ... means it can have an arbitrary number of leading
              dimensions.
  
      Returns:
          PIL Image or Tensor:  Vertically flipped image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.vflip(img)
  
      return F_t.vflip(img)
  
  
  def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
      """Crop the given image into four corners and the central crop.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      .. Note::
          This transform returns a tuple of images and there may be a
          mismatch in the number of inputs and targets your ``Dataset`` returns.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped.
          size (sequence or int): Desired output size of the crop. If size is an
              int instead of sequence like (h, w), a square crop (size, size) is
              made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
  
      Returns:
         tuple: tuple (tl, tr, bl, br, center)
         Corresponding top left, top right, bottom left, bottom right and center crop.
      """
      if isinstance(size, numbers.Number):
          size = (int(size), int(size))
      elif isinstance(size, (tuple, list)) and len(size) == 1:
          size = (size[0], size[0])
  
      if len(size) != 2:
          raise ValueError("Please provide only two dimensions (h, w) for size.")
  
      image_width, image_height = get_image_size(img)
      crop_height, crop_width = size
      if crop_width > image_width or crop_height > image_height:
          msg = "Requested crop size {} is bigger than input size {}"
          raise ValueError(msg.format(size, (image_height, image_width)))
  
      tl = crop(img, 0, 0, crop_height, crop_width)
      tr = crop(img, 0, image_width - crop_width, crop_height, crop_width)
      bl = crop(img, image_height - crop_height, 0, crop_height, crop_width)
      br = crop(img, image_height - crop_height, image_width - crop_width, crop_height, crop_width)
  
      center = center_crop(img, [crop_height, crop_width])
  
      return tl, tr, bl, br, center
  
  
  def ten_crop(img: Tensor, size: List[int], vertical_flip: bool = False) -> List[Tensor]:
      """Generate ten cropped images from the given image.
      Crop the given image into four corners and the central crop plus the
      flipped version of these (horizontal flipping is used by default).
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      .. Note::
          This transform returns a tuple of images and there may be a
          mismatch in the number of inputs and targets your ``Dataset`` returns.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped.
          size (sequence or int): Desired output size of the crop. If size is an
              int instead of sequence like (h, w), a square crop (size, size) is
              made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
          vertical_flip (bool): Use vertical flipping instead of horizontal
  
      Returns:
          tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
          Corresponding top left, top right, bottom left, bottom right and
          center crop and same for the flipped image.
      """
      if isinstance(size, numbers.Number):
          size = (int(size), int(size))
      elif isinstance(size, (tuple, list)) and len(size) == 1:
          size = (size[0], size[0])
  
      if len(size) != 2:
          raise ValueError("Please provide only two dimensions (h, w) for size.")
  
      first_five = five_crop(img, size)
  
      if vertical_flip:
          img = vflip(img)
      else:
          img = hflip(img)
  
      second_five = five_crop(img, size)
      return first_five + second_five
  
  
  def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
      """Adjust brightness of an image.
  
      Args:
          img (PIL Image or Tensor): Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
          brightness_factor (float):  How much to adjust the brightness. Can be
              any non negative number. 0 gives a black image, 1 gives the
              original image while 2 increases the brightness by a factor of 2.
  
      Returns:
          PIL Image or Tensor: Brightness adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_brightness(img, brightness_factor)
  
      return F_t.adjust_brightness(img, brightness_factor)
  
  
  def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
      """Adjust contrast of an image.
  
      Args:
          img (PIL Image or Tensor): Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
          contrast_factor (float): How much to adjust the contrast. Can be any
              non negative number. 0 gives a solid gray image, 1 gives the
              original image while 2 increases the contrast by a factor of 2.
  
      Returns:
          PIL Image or Tensor: Contrast adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_contrast(img, contrast_factor)
  
      return F_t.adjust_contrast(img, contrast_factor)
  
  
  def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
      """Adjust color saturation of an image.
  
      Args:
          img (PIL Image or Tensor): Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
          saturation_factor (float):  How much to adjust the saturation. 0 will
              give a black and white image, 1 will give the original image while
              2 will enhance the saturation by a factor of 2.
  
      Returns:
          PIL Image or Tensor: Saturation adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_saturation(img, saturation_factor)
  
      return F_t.adjust_saturation(img, saturation_factor)
  
  
  def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
      """Adjust hue of an image.
  
      The image hue is adjusted by converting the image to HSV and
      cyclically shifting the intensities in the hue channel (H).
      The image is then converted back to original image mode.
  
      `hue_factor` is the amount of shift in H channel and must be in the
      interval `[-0.5, 0.5]`.
  
      See `Hue`_ for more details.
  
      .. _Hue: https://en.wikipedia.org/wiki/Hue
  
      Args:
          img (PIL Image or Tensor): Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image mode "1", "I", "F" and modes with transparency (alpha channel) are not supported.
          hue_factor (float):  How much to shift the hue channel. Should be in
              [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in
              HSV space in positive and negative direction respectively.
              0 means no shift. Therefore, both -0.5 and 0.5 will give an image
              with complementary colors while 0 gives the original image.
  
      Returns:
          PIL Image or Tensor: Hue adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_hue(img, hue_factor)
  
      return F_t.adjust_hue(img, hue_factor)
  
  
  def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
      r"""Perform gamma correction on an image.
  
      Also known as Power Law Transform. Intensities in RGB mode are adjusted
      based on the following equation:
  
      .. math::
          I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}
  
      See `Gamma Correction`_ for more details.
  
      .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction
  
      Args:
          img (PIL Image or Tensor): PIL Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image, modes with transparency (alpha channel) are not supported.
          gamma (float): Non negative real number, same as :math:`\gamma` in the equation.
              gamma larger than 1 make the shadows darker,
              while gamma smaller than 1 make dark regions lighter.
          gain (float): The constant multiplier.
      Returns:
          PIL Image or Tensor: Gamma correction adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_gamma(img, gamma, gain)
  
      return F_t.adjust_gamma(img, gamma, gain)
  
  
  def _get_inverse_affine_matrix(
      center: List[float], angle: float, translate: List[float], scale: float, shear: List[float]
  ) -> List[float]:
      # Helper method to compute inverse matrix for affine transformation
  
      # As it is explained in PIL.Image.rotate
      # We need compute INVERSE of affine transformation matrix: M = T * C * RSS * C^-1
      # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1]
      #       C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1]
      #       RSS is rotation with scale and shear matrix
      #       RSS(a, s, (sx, sy)) =
      #       = R(a) * S(s) * SHy(sy) * SHx(sx)
      #       = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(x)/cos(y) - sin(a)), 0 ]
      #         [ s*sin(a + sy)/cos(sy), s*(-sin(a - sy)*tan(x)/cos(y) + cos(a)), 0 ]
      #         [ 0                    , 0                                      , 1 ]
      #
      # where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears:
      # SHx(s) = [1, -tan(s)] and SHy(s) = [1      , 0]
      #          [0, 1      ]              [-tan(s), 1]
      #
      # Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1
  
      rot = math.radians(angle)
      sx, sy = [math.radians(s) for s in shear]
  
      cx, cy = center
      tx, ty = translate
  
      # RSS without scaling
      a = math.cos(rot - sy) / math.cos(sy)
      b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot)
      c = math.sin(rot - sy) / math.cos(sy)
      d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot)
  
      # Inverted rotation matrix with scale and shear
      # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
      matrix = [d, -b, 0.0, -c, a, 0.0]
      matrix = [x / scale for x in matrix]
  
      # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
      matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty)
      matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty)
  
      # Apply center translation: C * RSS^-1 * C^-1 * T^-1
      matrix[2] += cx
      matrix[5] += cy
  
      return matrix
  
  
  def rotate(
      img: Tensor,
      angle: float,
      interpolation: InterpolationMode = InterpolationMode.NEAREST,
      expand: bool = False,
      center: Optional[List[int]] = None,
      fill: Optional[List[float]] = None,
      resample: Optional[int] = None,
  ) -> Tensor:
      """Rotate the image by angle.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
  
      Args:
          img (PIL Image or Tensor): image to be rotated.
          angle (number): rotation angle value in degrees, counter-clockwise.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
              If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
              For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
          expand (bool, optional): Optional expansion flag.
              If true, expands the output image to make it large enough to hold the entire rotated image.
              If false or omitted, make the output image the same size as the input image.
              Note that the expand flag assumes rotation around the center and no translation.
          center (sequence, optional): Optional center of rotation. Origin is the upper left corner.
              Default is the center of the image.
          fill (sequence or number, optional): Pixel fill value for the area outside the transformed
              image. If given a number, the value is used for all bands respectively.
  
              .. note::
                  In torchscript mode single int/float value is not supported, please use a sequence
                  of length 1: ``[value, ]``.
  
      Returns:
          PIL Image or Tensor: Rotated image.
  
      .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
  
      """
      if resample is not None:
          warnings.warn(
              "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
          )
          interpolation = _interpolation_modes_from_int(resample)
  
      # Backward compatibility with integer value
      if isinstance(interpolation, int):
          warnings.warn(
              "Argument interpolation should be of type InterpolationMode instead of int. "
              "Please, use InterpolationMode enum."
          )
          interpolation = _interpolation_modes_from_int(interpolation)
  
      if not isinstance(angle, (int, float)):
          raise TypeError("Argument angle should be int or float")
  
      if center is not None and not isinstance(center, (list, tuple)):
          raise TypeError("Argument center should be a sequence")
  
      if not isinstance(interpolation, InterpolationMode):
          raise TypeError("Argument interpolation should be a InterpolationMode")
  
      if not isinstance(img, torch.Tensor):
          pil_interpolation = pil_modes_mapping[interpolation]
          return F_pil.rotate(img, angle=angle, interpolation=pil_interpolation, expand=expand, center=center, fill=fill)
  
      center_f = [0.0, 0.0]
      if center is not None:
          img_size = get_image_size(img)
          # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center.
          center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, img_size)]
  
      # due to current incoherence of rotation angle direction between affine and rotate implementations
      # we need to set -angle.
      matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0])
      return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill)
  
  
  def affine(
      img: Tensor,
      angle: float,
      translate: List[int],
      scale: float,
      shear: List[float],
      interpolation: InterpolationMode = InterpolationMode.NEAREST,
      fill: Optional[List[float]] = None,
      resample: Optional[int] = None,
      fillcolor: Optional[List[float]] = None,
  ) -> Tensor:
      """Apply affine transformation on the image keeping image center invariant.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
  
      Args:
          img (PIL Image or Tensor): image to transform.
          angle (number): rotation angle in degrees between -180 and 180, clockwise direction.
          translate (sequence of integers): horizontal and vertical translations (post-rotation translation)
          scale (float): overall scale
          shear (float or sequence): shear angle value in degrees between -180 to 180, clockwise direction.
              If a sequence is specified, the first value corresponds to a shear parallel to the x axis, while
              the second value corresponds to a shear parallel to the y axis.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
              If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
              For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
          fill (sequence or number, optional): Pixel fill value for the area outside the transformed
              image. If given a number, the value is used for all bands respectively.
  
              .. note::
                  In torchscript mode single int/float value is not supported, please use a sequence
                  of length 1: ``[value, ]``.
          fillcolor (sequence, int, float): deprecated argument and will be removed since v0.10.0.
              Please use the ``fill`` parameter instead.
          resample (int, optional): deprecated argument and will be removed since v0.10.0.
              Please use the ``interpolation`` parameter instead.
  
      Returns:
          PIL Image or Tensor: Transformed image.
      """
      if resample is not None:
          warnings.warn(
              "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
          )
          interpolation = _interpolation_modes_from_int(resample)
  
      # Backward compatibility with integer value
      if isinstance(interpolation, int):
          warnings.warn(
              "Argument interpolation should be of type InterpolationMode instead of int. "
              "Please, use InterpolationMode enum."
          )
          interpolation = _interpolation_modes_from_int(interpolation)
  
      if fillcolor is not None:
          warnings.warn("Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead")
          fill = fillcolor
  
      if not isinstance(angle, (int, float)):
          raise TypeError("Argument angle should be int or float")
  
      if not isinstance(translate, (list, tuple)):
          raise TypeError("Argument translate should be a sequence")
  
      if len(translate) != 2:
          raise ValueError("Argument translate should be a sequence of length 2")
  
      if scale <= 0.0:
          raise ValueError("Argument scale should be positive")
  
      if not isinstance(shear, (numbers.Number, (list, tuple))):
          raise TypeError("Shear should be either a single value or a sequence of two values")
  
      if not isinstance(interpolation, InterpolationMode):
          raise TypeError("Argument interpolation should be a InterpolationMode")
  
      if isinstance(angle, int):
          angle = float(angle)
  
      if isinstance(translate, tuple):
          translate = list(translate)
  
      if isinstance(shear, numbers.Number):
          shear = [shear, 0.0]
  
      if isinstance(shear, tuple):
          shear = list(shear)
  
      if len(shear) == 1:
          shear = [shear[0], shear[0]]
  
      if len(shear) != 2:
          raise ValueError("Shear should be a sequence containing two values. Got {}".format(shear))
  
      img_size = get_image_size(img)
      if not isinstance(img, torch.Tensor):
          # center = (img_size[0] * 0.5 + 0.5, img_size[1] * 0.5 + 0.5)
          # it is visually better to estimate the center without 0.5 offset
          # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
          center = [img_size[0] * 0.5, img_size[1] * 0.5]
          matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
          pil_interpolation = pil_modes_mapping[interpolation]
          return F_pil.affine(img, matrix=matrix, interpolation=pil_interpolation, fill=fill)
  
      translate_f = [1.0 * t for t in translate]
      matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, translate_f, scale, shear)
      return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill)
  
  
  @torch.jit.unused
  def to_grayscale(img, num_output_channels=1):
      """Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image.
      This transform does not support torch Tensor.
  
      Args:
          img (PIL Image): PIL Image to be converted to grayscale.
          num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1.
  
      Returns:
          PIL Image: Grayscale version of the image.
  
          - if num_output_channels = 1 : returned image is single channel
          - if num_output_channels = 3 : returned image is 3 channel with r = g = b
      """
      if isinstance(img, Image.Image):
          return F_pil.to_grayscale(img, num_output_channels)
  
      raise TypeError("Input should be PIL Image")
  
  
  def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:
      """Convert RGB image to grayscale version of image.
      If the image is torch Tensor, it is expected
      to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
  
      Note:
          Please, note that this method supports only RGB images as input. For inputs in other color spaces,
          please, consider using meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image.
  
      Args:
          img (PIL Image or Tensor): RGB Image to be converted to grayscale.
          num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1.
  
      Returns:
          PIL Image or Tensor: Grayscale version of the image.
  
          - if num_output_channels = 1 : returned image is single channel
          - if num_output_channels = 3 : returned image is 3 channel with r = g = b
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.to_grayscale(img, num_output_channels)
  
      return F_t.rgb_to_grayscale(img, num_output_channels)
  
  
  def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor:
      """Erase the input Tensor Image with given value.
      This transform does not support PIL Image.
  
      Args:
          img (Tensor Image): Tensor image of size (C, H, W) to be erased
          i (int): i in (i,j) i.e coordinates of the upper left corner.
          j (int): j in (i,j) i.e coordinates of the upper left corner.
          h (int): Height of the erased region.
          w (int): Width of the erased region.
          v: Erasing value.
          inplace(bool, optional): For in-place operations. By default is set False.
  
      Returns:
          Tensor Image: Erased image.
      """
      if not isinstance(img, torch.Tensor):
          raise TypeError("img should be Tensor Image. Got {}".format(type(img)))
  
      if not inplace:
          img = img.clone()
  
      img[..., i : i + h, j : j + w] = v
      return img
  
  
  def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor:
      """Performs Gaussian blurring on the image by given kernel.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
  
      Args:
          img (PIL Image or Tensor): Image to be blurred
          kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers
              like ``(kx, ky)`` or a single integer for square kernels.
  
              .. note::
                  In torchscript mode kernel_size as single int is not supported, use a sequence of
                  length 1: ``[ksize, ]``.
          sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a
              sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the
              same sigma in both X/Y directions. If None, then it is computed using
              ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``.
              Default, None.
  
              .. note::
                  In torchscript mode sigma as single float is
                  not supported, use a sequence of length 1: ``[sigma, ]``.
  
      Returns:
          PIL Image or Tensor: Gaussian Blurred version of the image.
      """
      if not isinstance(kernel_size, (int, list, tuple)):
          raise TypeError("kernel_size should be int or a sequence of integers. Got {}".format(type(kernel_size)))
      if isinstance(kernel_size, int):
          kernel_size = [kernel_size, kernel_size]
      if len(kernel_size) != 2:
          raise ValueError("If kernel_size is a sequence its length should be 2. Got {}".format(len(kernel_size)))
      for ksize in kernel_size:
          if ksize % 2 == 0 or ksize < 0:
              raise ValueError("kernel_size should have odd and positive integers. Got {}".format(kernel_size))
  
      if sigma is None:
          sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]
  
      if sigma is not None and not isinstance(sigma, (int, float, list, tuple)):
          raise TypeError("sigma should be either float or sequence of floats. Got {}".format(type(sigma)))
      if isinstance(sigma, (int, float)):
          sigma = [float(sigma), float(sigma)]
      if isinstance(sigma, (list, tuple)) and len(sigma) == 1:
          sigma = [sigma[0], sigma[0]]
      if len(sigma) != 2:
          raise ValueError("If sigma is a sequence, its length should be 2. Got {}".format(len(sigma)))
      for s in sigma:
          if s <= 0.0:
              raise ValueError("sigma should have positive values. Got {}".format(sigma))
  
      t_img = img
      if not isinstance(img, torch.Tensor):
          if not F_pil._is_pil_image(img):
              raise TypeError("img should be PIL Image or Tensor. Got {}".format(type(img)))
  
          t_img = to_tensor(img)
  
      output = F_t.gaussian_blur(t_img, kernel_size, sigma)
  
      if not isinstance(img, torch.Tensor):
          output = to_pil_image(output)
      return output
  
  
  def invert(img: Tensor) -> Tensor:
      """Invert the colors of an RGB/grayscale image.
  
      Args:
          img (PIL Image or Tensor): Image to have its colors inverted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image, it is expected to be in mode "L" or "RGB".
  
      Returns:
          PIL Image or Tensor: Color inverted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.invert(img)
  
      return F_t.invert(img)
  
  
  def posterize(img: Tensor, bits: int) -> Tensor:
      """Posterize an image by reducing the number of bits for each color channel.
  
      Args:
          img (PIL Image or Tensor): Image to have its colors posterized.
              If img is torch Tensor, it should be of type torch.uint8 and
              it is expected to be in [..., 1 or 3, H, W] format, where ... means
              it can have an arbitrary number of leading dimensions.
              If img is PIL Image, it is expected to be in mode "L" or "RGB".
          bits (int): The number of bits to keep for each channel (0-8).
      Returns:
          PIL Image or Tensor: Posterized image.
      """
      if not (0 <= bits <= 8):
          raise ValueError("The number if bits should be between 0 and 8. Got {}".format(bits))
  
      if not isinstance(img, torch.Tensor):
          return F_pil.posterize(img, bits)
  
      return F_t.posterize(img, bits)
  
  
  def solarize(img: Tensor, threshold: float) -> Tensor:
      """Solarize an RGB/grayscale image by inverting all pixel values above a threshold.
  
      Args:
          img (PIL Image or Tensor): Image to have its colors inverted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image, it is expected to be in mode "L" or "RGB".
          threshold (float): All pixels equal or above this value are inverted.
      Returns:
          PIL Image or Tensor: Solarized image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.solarize(img, threshold)
  
      return F_t.solarize(img, threshold)
  
  
  def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
      """Adjust the sharpness of an image.
  
      Args:
          img (PIL Image or Tensor): Image to be adjusted.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
          sharpness_factor (float):  How much to adjust the sharpness. Can be
              any non negative number. 0 gives a blurred image, 1 gives the
              original image while 2 increases the sharpness by a factor of 2.
  
      Returns:
          PIL Image or Tensor: Sharpness adjusted image.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.adjust_sharpness(img, sharpness_factor)
  
      return F_t.adjust_sharpness(img, sharpness_factor)
  
  
  def autocontrast(img: Tensor) -> Tensor:
      """Maximize contrast of an image by remapping its
      pixels per channel so that the lowest becomes black and the lightest
      becomes white.
  
      Args:
          img (PIL Image or Tensor): Image on which autocontrast is applied.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image, it is expected to be in mode "L" or "RGB".
  
      Returns:
          PIL Image or Tensor: An image that was autocontrasted.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.autocontrast(img)
  
      return F_t.autocontrast(img)
  
  
  def equalize(img: Tensor) -> Tensor:
      """Equalize the histogram of an image by applying
      a non-linear mapping to the input in order to create a uniform
      distribution of grayscale values in the output.
  
      Args:
          img (PIL Image or Tensor): Image on which equalize is applied.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              The tensor dtype must be ``torch.uint8`` and values are expected to be in ``[0, 255]``.
              If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".
  
      Returns:
          PIL Image or Tensor: An image that was equalized.
      """
      if not isinstance(img, torch.Tensor):
          return F_pil.equalize(img)
  
      return F_t.equalize(img)
  