code: def _blend(
      image1: torch.Tensor, image2: torch.Tensor, ratio: float
  ) -> torch.Tensor:
      ratio = float(ratio)
      fp = image1.is_floating_point()
      bound = _max_value(image1.dtype)
      output = (
          image1.mul(ratio)
          .add_(image2, alpha=(1.0 - ratio))
          .clamp_(0, bound)
      )
      return output if fp else output.to(image1.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 5 for calling a global function with example usage: _blend(image1, image2, ratio) and returns: unknown