code: def test_gradcheck(self, batch_size, device, dtype):
      H, W = 10, 20
      fx, fy = W, H
      cx, cy = W / 2, H / 2
      eps = 1e-12
      seed = 77
      low, high = -500, 500
      atol, rtol = 1e-5, 1e-3
  
      # Different tolerances for the below case.
      if (device.type == "cuda") and (dtype == torch.float64):
          atol, rtol = 1e-4, 1e-2
  
      # If contiguous() is not called, gradcheck fails
      intrinsics = self._create_intrinsics(
          batch_size, fx, fy, cx, cy, device=device, dtype=dtype
      ).contiguous()
  
      # Setting the projection matrix to the intrinsic matrix for
      # simplicity (i.e. assuming that the RT matrix is an identity matrix)
      proj_mat = intrinsics
  
      torch.manual_seed(seed)
      cam_coords_src = self._get_samples(
          (batch_size, H, W, 3), low, high, device, dtype
      )
  
      cam_coords_src = tensor_to_gradcheck_var(cam_coords_src)
      proj_mat = tensor_to_gradcheck_var(proj_mat)
  
      assert gradcheck(
          kornia.geometry.camera.cam2pixel,
          (cam_coords_src, proj_mat, eps),
          raise_exception=True,
          atol=atol,
          rtol=rtol,
          fast_mode=True,
      )
  
imports: import math
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.testing import assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 6 for calling the TestCam2Pixel obj's underlying member method code with example usage: obj.test_gradcheck(batch_size, device, dtype) without return types