class_docstr: Apply a random vertical flip to a tensor image or a batch of tensor images with a given probability.
  
  .. image:: _static/img/RandomVerticalFlip.png
  
  Args:
      p: probability of the image being flipped.
      same_on_batch: apply the same transformation across the batch.
      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.vflip`.
  
  Examples:
      >>> import torch
      >>> input = torch.tensor([[[[0., 0., 0.],
      ...                         [0., 0., 0.],
      ...                         [0., 1., 1.]]]])
      >>> seq = RandomVerticalFlip(p=1.0)
      >>> seq(input), seq.transform_matrix
      (tensor([[[[0., 1., 1.],
                [0., 0., 0.],
                [0., 0., 0.]]]]), tensor([[[ 1.,  0.,  0.],
               [ 0., -1.,  2.],
               [ 0.,  0.,  1.]]]))
      >>> seq.inverse(seq(input)).equal(input)
      True
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> seq = RandomVerticalFlip(p=1.0)
      >>> (seq(input) == seq(input, params=seq._params)).all()
      tensor(True)
code: def inverse_transform(
      self,
      input: Tensor,
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
      size: Optional[Tuple[int, int]] = None,
  ) -> Tensor:
      if not isinstance(transform, Tensor):
          raise TypeError(
              f"Expected the `transform` be a Tensor. Got {type(transform)}."
          )
      return self.apply_transform(
          input,
          params=self._params,
          transform=as_tensor(
              transform, device=input.device, dtype=input.dtype
          ),
          flags=flags,
      )
  
file_path: kornia/kornia/augmentation/_2d/geometric/vertical_flip.py
imports: from typing import Any, Dict, Optional, Tuple
  from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
  from kornia.core import Tensor, as_tensor, tensor
  from kornia.geometry.transform import vflip
label_desc: Use image node_id 3 for calling the RandomVerticalFlip obj's underlying member method code with example usage: obj.inverse_transform(input, flags, transform, size) and returns: self