code: def intrinsics_like(focal: float, input: Tensor) -> Tensor:
      r"""Return a 3x3 intrinsics matrix, with same size as the input.
  
      The center of projection will be based in the input image size.
  
      Args:
          focal: the focal length for the camera matrix.
          input: image tensor that will determine the batch size and image height
            and width. It is assumed to be a tensor in the shape of :math:`(B, C, H, W)`.
  
      Returns:
          The camera matrix with the shape of :math:`(B, 3, 3)`.
      """
      if len(input.shape) != 4:
          raise AssertionError(input.shape)
      if focal <= 0:
          raise AssertionError(focal)
  
      _, _, H, W = input.shape
  
      intrinsics = eye_like(3, input)
      intrinsics[..., 0, 0] *= focal
      intrinsics[..., 1, 1] *= focal
      intrinsics[..., 0, 2] += 1.0 * W / 2
      intrinsics[..., 1, 2] += 1.0 * H / 2
      return intrinsics
  
file_path: kornia/kornia/geometry/epipolar/projection.py
imports: from typing import Tuple, Union
  import torch
  from torch.linalg import qr
  from kornia.core import Tensor, concatenate, ones_like, pad, stack, zeros_like
  from kornia.utils import eye_like, vec_like
  from kornia.utils.helpers import _torch_svd_cast
  from .numeric import cross_product_matrix
label_desc: Use image node_id 1 for calling a global function with example usage: intrinsics_like(focal, input) and returns: intrinsics