code: def test_consistency(self, batch_size, device, dtype):
      H, W = 250, 500
      fx, fy = W, H
      cx, cy = W / 2, H / 2
      eps = 1e-12
      seed = 77
      low, high = -500, 500
  
      intrinsics = self._create_intrinsics(
          batch_size, fx, fy, cx, cy, device=device, dtype=dtype
      )
      intrinsics_inv = self._create_intrinsics_inv(
          batch_size, fx, fy, cx, cy, device=device, dtype=dtype
      )
  
      # 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_input = self._get_samples(
          (batch_size, H, W, 3), low, high, device, dtype
      )
  
      pixel_coords_output = kornia.geometry.camera.cam2pixel(
          cam_coords_src=cam_coords_input,
          dst_proj_src=proj_mat,
          eps=eps,
      )
  
      last_ch = torch.ones(
          (batch_size, H, W, 1), device=device, dtype=dtype
      )
      pixel_coords_concat = torch.cat(
          [pixel_coords_output, last_ch], axis=-1
      )
  
      depth = (
          cam_coords_input[..., 2:3].permute(0, 3, 1, 2).contiguous()
      )
      cam_coords_output = kornia.geometry.camera.pixel2cam(
          depth=depth,
          intrinsics_inv=intrinsics_inv,
          pixel_coords=pixel_coords_concat,
      )
  
      assert_close(
          cam_coords_output, cam_coords_input, atol=1e-4, rtol=1e-4
      )
  
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 5 for calling the TestCam2Pixel obj's underlying member method code with example usage: obj.test_consistency(batch_size, device, dtype) without return types