text: from typing import Dict, Optional, Tuple, Union, cast
  
  import torch
  
  from kornia.augmentation import random_generator as rg
  from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
  from kornia.constants import Resample, SamplePadding
  from kornia.geometry.conversions import deg2rad
  from kornia.geometry.transform import get_affine_matrix2d, warp_affine
  
  
  class RandomAffine(GeometricAugmentationBase2D):
      r"""Apply a random 2D affine transformation to a tensor image.
  
      .. image:: _static/img/RandomAffine.png
  
      The transformation is computed so that the image center is kept invariant.
  
      Args:
          p: probability of applying the transformation.
          degrees: Range of degrees to select from.
              If degrees is a number instead of sequence like (min, max), the range of degrees
              will be (-degrees, +degrees). Set to 0 to deactivate rotations.
          translate: tuple of maximum absolute fraction for horizontal
              and vertical translations. For example translate=(a, b), then horizontal shift
              is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
              randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
          scale: scaling factor interval.
              If (a, b) represents isotropic scaling, the scale is randomly sampled from the range a <= scale <= b.
              If (a, b, c, d), the scale is randomly sampled from the range a <= scale_x <= b, c <= scale_y <= d.
              Will keep original scale by default.
          shear: Range of degrees to select from.
              If float, a shear parallel to the x axis in the range (-shear, +shear) will be applied.
              If (a, b), a shear parallel to the x axis in the range (-shear, +shear) will be applied.
              If (a, b, c, d), then x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3])
              will be applied. Will not apply shear by default.
          resample: resample mode from "nearest" (0) or "bilinear" (1).
          padding_mode: padding mode from "zeros" (0), "border" (1) or "refection" (2).
          return_transform: if ``True`` return the matrix describing the transformation applied to each.
          same_on_batch: apply the same transformation across the batch.
          align_corners: interpolation flag.
          keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False).
  
      Shape:
          - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
          - Output: :math:`(B, C, H, W)`
  
      .. note::
          This function internally uses :func:`kornia.geometry.transform.warp_affine`.
  
      Examples:
          >>> rng = torch.manual_seed(0)
          >>> input = torch.rand(1, 1, 3, 3)
          >>> aug = RandomAffine((-15., 20.), return_transform=True, p=1.)
          >>> out = aug(input)
          >>> out
          (tensor([[[[0.3961, 0.7310, 0.1574],
                    [0.1781, 0.3074, 0.5648],
                    [0.4804, 0.8379, 0.4234]]]]), tensor([[[ 0.9923, -0.1241,  0.1319],
                   [ 0.1241,  0.9923, -0.1164],
                   [ 0.0000,  0.0000,  1.0000]]]))
          >>> aug.inverse(out)
          tensor([[[[0.3890, 0.6573, 0.1865],
                    [0.2063, 0.3074, 0.5459],
                    [0.3892, 0.7896, 0.4224]]]])
          >>> input
          tensor([[[[0.4963, 0.7682, 0.0885],
                    [0.1320, 0.3074, 0.6341],
                    [0.4901, 0.8964, 0.4556]]]])
  
      To apply the exact augmenation again, you may take the advantage of the previous parameter state:
          >>> input = torch.randn(1, 3, 32, 32)
          >>> aug = RandomAffine((-15., 20.), p=1.)
          >>> (aug(input) == aug(input, params=aug._params)).all()
          tensor(True)
      """
  
      def __init__(
          self,
          degrees: Union[torch.Tensor, float, Tuple[float, float]],
          translate: Optional[Union[torch.Tensor, Tuple[float, float]]] = None,
          scale: Optional[Union[torch.Tensor, Tuple[float, float], Tuple[float, float, float, float]]] = None,
          shear: Optional[Union[torch.Tensor, float, Tuple[float, float]]] = None,
          resample: Union[str, int, Resample] = Resample.BILINEAR.name,
          return_transform: bool = False,
          same_on_batch: bool = False,
          align_corners: bool = False,
          padding_mode: Union[str, int, SamplePadding] = SamplePadding.ZEROS.name,
          p: float = 0.5,
          keepdim: bool = False,
      ) -> None:
          super().__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch, keepdim=keepdim)
          self._param_generator = cast(rg.AffineGenerator, rg.AffineGenerator(degrees, translate, scale, shear))
          self.flags = dict(
              resample=Resample.get(resample), padding_mode=SamplePadding.get(padding_mode), align_corners=align_corners
          )
  
      def compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
          return get_affine_matrix2d(
              torch.as_tensor(params["translations"], device=input.device, dtype=input.dtype),
              torch.as_tensor(params["center"], device=input.device, dtype=input.dtype),
              torch.as_tensor(params["scale"], device=input.device, dtype=input.dtype),
              torch.as_tensor(params["angle"], device=input.device, dtype=input.dtype),
              deg2rad(torch.as_tensor(params["sx"], device=input.device, dtype=input.dtype)),
              deg2rad(torch.as_tensor(params["sy"], device=input.device, dtype=input.dtype)),
          )
  
      def apply_transform(
          self, input: torch.Tensor, params: Dict[str, torch.Tensor], transform: Optional[torch.Tensor] = None
      ) -> torch.Tensor:
          _, _, height, width = input.shape
          transform = cast(torch.Tensor, transform)
          return warp_affine(
              input,
              transform[:, :2, :],
              (height, width),
              self.flags["resample"].name.lower(),
              align_corners=self.flags["align_corners"],
              padding_mode=self.flags["padding_mode"].name.lower(),
          )
  
      def inverse_transform(
          self,
          input: torch.Tensor,
          transform: Optional[torch.Tensor] = None,
          size: Optional[Tuple[int, int]] = None,
          **kwargs,
      ) -> torch.Tensor:
          return self.apply_transform(
              input, params=self._params, transform=torch.as_tensor(transform, device=input.device, dtype=input.dtype)
          )
  