code: def adjust_saturation_image(
      image: torch.Tensor, saturation_factor: float
  ) -> torch.Tensor:
      if saturation_factor < 0:
          raise ValueError(
              f"saturation_factor ({saturation_factor}) is not non-negative."
          )
  
      c = image.shape[-3]
      if c not in [1, 3]:
          raise TypeError(
              f"Input image tensor permitted channel values are 1 or 3, but found {c}"
          )
  
      if c == 1:  # Match PIL behaviour
          return image
  
      grayscale_image = _rgb_to_grayscale_image(
          image, num_output_channels=1, preserve_dtype=False
      )
      if not image.is_floating_point():
          grayscale_image = grayscale_image.floor_()
  
      return _blend(image, grayscale_image, saturation_factor)
  
imports: from typing import List
  import PIL.Image
  import torch
  from torch.nn.functional import conv2d
  from torchvision import tv_tensors
  from torchvision.transforms import _functional_pil
  from torchvision.transforms._functional_tensor import _max_value
  from torchvision.utils import _log_api_usage_once
  from ._misc import _num_value_bits, to_dtype_image
  from ._type_conversion import pil_to_tensor, to_pil_image
  from ._utils import _get_kernel, _register_kernel_internal
label_desc: Use image node_id 11 for calling a global function with example usage: adjust_saturation_image(image, saturation_factor) and returns: _blend, image