class_docstr: Class that represents a list of pinhole cameras.
  
  The class inherits from :class:`~kornia.PinholeCamera` meaning that
  it will keep the same class properties but with an extra dimension.
  
  .. note::
      The underlying data tensor will be stacked in the first dimension.
      That's it, given a list of two camera instances, the intrinsics tensor
      will have a shape :math:`(B, N, 4, 4)` where :math:`B` is the batch
      size and :math:`N` is the numbers of cameras (in this case two).
  
  Args:
      pinholes_list: a python tuple or list containing a set of `PinholeCamera` instances.
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docstr: Return a PinholeCamera object with parameters such as Bx4x4.
docstr_tok: ["Return","a","PinholeCamera","object","with","parameters","such","as","Bx4x4","."]
code: def get_pinhole(self, idx: int) -> PinholeCamera:
      r"""Return a PinholeCamera object with parameters such as Bx4x4."""
      height: Tensor = self.height[..., idx]
      width: Tensor = self.width[..., idx]
      intrinsics: Tensor = self.intrinsics[:, idx]
      extrinsics: Tensor = self.extrinsics[:, idx]
      return PinholeCamera(intrinsics, extrinsics, height, width)
  
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 4 for calling the PinholeCamerasList obj's underlying member method code with example usage: obj.get_pinhole(idx) and returns: PinholeCamera