code: def adjust_brightness_image(
      image: torch.Tensor, brightness_factor: float
  ) -> torch.Tensor:
      if brightness_factor < 0:
          raise ValueError(
              f"brightness_factor ({brightness_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}"
          )
  
      fp = image.is_floating_point()
      bound = _max_value(image.dtype)
      output = image.mul(brightness_factor).clamp_(0, bound)
      return output if fp else output.to(image.dtype)
  
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 7 for calling a global function with example usage: adjust_brightness_image(image, brightness_factor) and returns: unknown