code: def rgb_to_grayscale(
      img: Tensor, num_output_channels: int = 1
  ) -> Tensor:
      """Convert RGB image to grayscale version of image.
      If the image is torch Tensor, it is expected
      to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
  
      Note:
          Please, note that this method supports only RGB images as input. For inputs in other color spaces,
          please, consider using :meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image.
  
      Args:
          img (PIL Image or Tensor): RGB Image to be converted to grayscale.
          num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1.
  
      Returns:
          PIL Image or Tensor: Grayscale version of the image.
  
          - if num_output_channels = 1 : returned image is single channel
          - if num_output_channels = 3 : returned image is 3 channel with r = g = b
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(rgb_to_grayscale)
      if not isinstance(img, torch.Tensor):
          return F_pil.to_grayscale(img, num_output_channels)
  
      return F_t.rgb_to_grayscale(img, num_output_channels)
  
imports: import math
  import numbers
  import sys
  import warnings
  from enum import Enum
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from torch import Tensor
  from ..utils import _log_api_usage_once
  from .None import _functional_pil, _functional_tensor
label_desc: Use image node_id 33 for calling a global function with example usage: rgb_to_grayscale(img, num_output_channels) and returns: F_t, F_pil