class_docstr: AugmentationBase2D base class for rigid/affine augmentation implementations.
  
  RigidAffineAugmentationBase2D enables routined transformation with given transformation matrices
  for different data types like masks, boxes, and keypoints.
  
  Args:
      p: probability for applying an augmentation. This param controls the augmentation probabilities
        element-wise for a batch.
      p_batch: probability for applying an augmentation to a batch. This param controls the augmentation
        probabilities batch-wise.
      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``.
code: def compute_transformation(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
  ) -> Tensor:
      raise NotImplementedError
  
imports: from typing import Any, Dict, Optional
  from torch import float16, float32, float64
  import kornia
  from kornia.augmentation.base import _AugmentationBase
  from kornia.augmentation.utils import _transform_input3d, _validate_input_dtype
  from kornia.core import Tensor
  from kornia.geometry.boxes import Boxes3D
  from kornia.geometry.keypoints import Keypoints3D
label_desc: Use image node_id 2 for calling the RigidAffineAugmentationBase3D obj's underlying member method code with example usage: obj.compute_transformation(input, params, flags) without return types