code: def to_grayscale(img, num_output_channels=1):
      """Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image.
      This transform does not support torch Tensor.
  
      Args:
          img (PIL Image): PIL Image to be converted to grayscale.
          num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1.
  
      Returns:
          PIL Image: 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(to_grayscale)
      if isinstance(img, Image.Image):
          return F_pil.to_grayscale(img, num_output_channels)
  
      raise TypeError("Input should be PIL Image")
  
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 32 for calling a global function with example usage: to_grayscale(img, num_output_channels) and returns: F_pil