class_docstr: Apply random horizontal flip to 3D volumes (5D tensor).
  
  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, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)`
      - Output: :math:`(B, C, D, H, W)`
  
  Note:
      Input tensor must be float and normalized into [0, 1] for the best differentiability support.
      Additionally, this function accepts another transformation tensor (:math:`(B, 4, 4)`), then the
      applied transformation will be merged int to the input transformation tensor and returned.
  
  Examples:
      >>> import torch
      >>> x = torch.eye(3).repeat(3, 1, 1)
      >>> seq = RandomHorizontalFlip3D(p=1.0)
      >>> seq(x), seq.transform_matrix
      (tensor([[[[[0., 0., 1.],
                 [0., 1., 0.],
                 [1., 0., 0.]],
      <BLANKLINE>
                [[0., 0., 1.],
                 [0., 1., 0.],
                 [1., 0., 0.]],
      <BLANKLINE>
                [[0., 0., 1.],
                 [0., 1., 0.],
                 [1., 0., 0.]]]]]), tensor([[[-1.,  0.,  0.,  2.],
               [ 0.,  1.,  0.,  0.],
               [ 0.,  0.,  1.,  0.],
               [ 0.,  0.,  0.,  1.]]]))
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.rand(1, 3, 32, 32, 32)
      >>> aug = RandomHorizontalFlip3D(p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def compute_transformation(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
  ) -> Tensor:
      w: int = input.shape[-1]
      flip_mat: Tensor = torch.tensor(
          [[-1, 0, 0, w - 1], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
          device=input.device,
          dtype=input.dtype,
      )
      return flip_mat.expand(input.shape[0], 4, 4)
  
file_path: kornia/kornia/augmentation/_3d/geometric/horizontal_flip.py
imports: from typing import Any, Dict, Optional
  import torch
  from torch import Tensor
  from kornia.augmentation._3d.geometric.base import GeometricAugmentationBase3D
label_desc: Use image node_id 2 for calling the RandomHorizontalFlip3D obj's underlying member method code with example usage: obj.compute_transformation(input, params, flags) and returns: flip_mat