class_docstr: Class that represents a Pinhole Camera model.
  
  Args:
      intrinsics: tensor with shape :math:`(B, 4, 4)`
        containing the full 4x4 camera calibration matrix.
      extrinsics: tensor with shape :math:`(B, 4, 4)`
        containing the full 4x4 rotation-translation matrix.
      height: tensor with shape :math:`(B)` containing the image height.
      width: tensor with shape :math:`(B)` containing the image width.
  
  .. note::
      We assume that the class attributes are in batch form in order to take
      advantage of PyTorch parallelism to boost computing performance.
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docstr: Return the 3x4 rotation-translation matrix.
  
  Returns:
      tensor of shape :math:`(B, 3, 4)`.
docstr_tok: ["Return","the","3x4","rotation-translation","matrix",".","Returns",":","tensor","of","shape",":","math",":","`","(","B",",","3",",","4",")","`","."]
code: def rt_matrix(self) -> Tensor:
      r"""Return the 3x4 rotation-translation matrix.
  
      Returns:
          tensor of shape :math:`(B, 3, 4)`.
      """
      return self.extrinsics[..., :3, :4]
  
imports: from typing import Iterable, List, Union
  import torch
  from kornia.core import Device, Tensor, eye, stack, zeros
  from kornia.core.check import KORNIA_CHECK_SAME_DEVICE
  from kornia.geometry.conversions import convert_points_from_homogeneous, convert_points_to_homogeneous
  from kornia.geometry.linalg import inverse_transformation, transform_points
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 20 for calling the PinholeCamera obj's underlying member method code with example usage: obj.rt_matrix() and returns: self