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 the number of pinholes cameras per batch.
docstr_tok: ["Return","the","number","of","pinholes","cameras","per","batch","."]
code: def num_cameras(self) -> int:
      r"""Return the number of pinholes cameras per batch."""
      num_cameras: int = -1
      if self.intrinsics is not None:
          num_cameras = int(self.intrinsics.shape[1])
      return num_cameras
  
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 3 for calling the PinholeCamerasList obj's underlying member method code with example usage: obj.num_cameras() and returns: num_cameras