code: def denormalize_points_with_intrinsics(
      point_2d_norm: Tensor, camera_matrix: Tensor
  ) -> Tensor:
      """Normalizes points with intrinsics. Useful for conversion of keypoints to be used with essential matrix.
  
      Args:
          point_2d_norm: tensor containing the 2d points in the image pixel coordinates. The shape of the tensor can be
                         :math:`(*, 2)`.
          camera_matrix: tensor containing the intrinsics camera matrix. The tensor shape must be :math:`(*, 3, 3)`.
  
      Returns:
          tensor of (u, v) cam coordinates with shape :math:`(*, 2)`.
  
      Example:
          >>> _ = torch.manual_seed(0)
          >>> X = torch.rand(1, 2)
          >>> K = torch.eye(3)[None]
          >>> denormalize_points_with_intrinsics(X, K)
          tensor([[0.4963, 0.7682]])
      """
      KORNIA_CHECK_SHAPE(point_2d_norm, ["*", "2"])
      KORNIA_CHECK_SHAPE(camera_matrix, ["*", "3", "3"])
      # projection eq. [u, v, w]' = K * [x y z 1]'
      # u = fx * X + cx
      # v = fy * Y + cy
  
      # unpack coordinates
      x_coord: Tensor = point_2d_norm[..., 0]
      y_coord: Tensor = point_2d_norm[..., 1]
  
      # unpack intrinsics
      fx: Tensor = camera_matrix[..., 0, 0]
      fy: Tensor = camera_matrix[..., 1, 1]
      cx: Tensor = camera_matrix[..., 0, 2]
      cy: Tensor = camera_matrix[..., 1, 2]
  
      # apply intrinsics ans return
      u_coord: Tensor = x_coord * fx + cx
      v_coord: Tensor = y_coord * fy + cy
  
      return stack([u_coord, v_coord], dim=-1)
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from kornia.constants import pi
  from kornia.core import Tensor, concatenate, cos, pad, sin, stack, tensor, where, zeros_like
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SHAPE
  from kornia.utils import deprecated
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 36 for calling a global function with example usage: denormalize_points_with_intrinsics(point_2d_norm, camera_matrix) and returns: stack