code: def test_rgb_to_grayscale(device, num_output_channels):
      script_rgb_to_grayscale = torch.jit.script(F.rgb_to_grayscale)
  
      img_tensor, pil_img = _create_data(32, 34, device=device)
  
      gray_pil_image = F.rgb_to_grayscale(
          pil_img, num_output_channels=num_output_channels
      )
      gray_tensor = F.rgb_to_grayscale(
          img_tensor, num_output_channels=num_output_channels
      )
  
      _assert_approx_equal_tensor_to_pil(
          gray_tensor.float(),
          gray_pil_image,
          tol=1.0 + 1e-10,
          agg_method="max",
      )
  
      s_gray_tensor = script_rgb_to_grayscale(
          img_tensor, num_output_channels=num_output_channels
      )
      assert_equal(s_gray_tensor, gray_tensor)
  
      batch_tensors = _create_data_batch(
          16, 18, num_samples=4, device=device
      )
      _test_fn_on_batch(
          batch_tensors,
          F.rgb_to_grayscale,
          num_output_channels=num_output_channels,
      )
  
imports: import colorsys
  import itertools
  import math
  import os
  from functools import partial
  from typing import Sequence
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torchvision.transforms
  import torchvision.transforms._functional_pil
  import torchvision.transforms._functional_tensor
  import torchvision.transforms.functional
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, _test_fn_on_batch, assert_equal, cpu_and_cuda, needs_cuda
  from torchvision.transforms import InterpolationMode
label_desc: Use image node_id 35 for calling a global function with example usage: test_rgb_to_grayscale(device, num_output_channels) without return types