class_docstr: Apply random flip along the depth axis of 3D volumes (5D tensor).
  
  Input should be a tensor of shape :math:`(C, D, H, W)` or a batch of tensors :math:`(*, C, D, H, W)`.
  If Input is a tuple it is assumed that the first element contains the aforementioned tensors and the second,
  the corresponding transformation matrix that has been applied to them. In this case the module
  will Depthically flip the tensors and concatenate the corresponding transformation matrix to the
  previous one. This is especially useful when using this functionality as part of an ``nn.Sequential`` module.
  
  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 = RandomDepthicalFlip3D(p=1.0)
      >>> seq(x), seq.transform_matrix
      (tensor([[[[[1., 0., 0.],
                 [0., 1., 0.],
                 [0., 0., 1.]],
      <BLANKLINE>
                [[1., 0., 0.],
                 [0., 1., 0.],
                 [0., 0., 1.]],
      <BLANKLINE>
                [[1., 0., 0.],
                 [0., 1., 0.],
                 [0., 0., 1.]]]]]), tensor([[[ 1.,  0.,  0.,  0.],
               [ 0.,  1.,  0.,  0.],
               [ 0.,  0., -1.,  2.],
               [ 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 = RandomDepthicalFlip3D(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:
      d: int = input.shape[-3]
      flip_mat: Tensor = torch.tensor(
          [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, -1, d - 1], [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/depthical_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 RandomDepthicalFlip3D obj's underlying member method code with example usage: obj.compute_transformation(input, params, flags) and returns: flip_mat