code: def blend(
      image1: TensorLike, image2: TensorLike, factor: Number
  ) -> tf.Tensor:
      """Blend `image1` and `image2` using `factor`.
  
      Factor can be above 0.0.  A value of 0.0 means only `image1` is used.
      A value of 1.0 means only `image2` is used.  A value between 0.0 and
      1.0 means we linearly interpolate the pixel values between the two
      images.  A value greater than 1.0 "extrapolates" the difference
      between the two pixel values, and we clip the results to values
      between 0 and 255.
  
      Args:
        image1: An image Tensor of shape
            `(num_rows, num_columns, num_channels)` (HWC), or
            `(num_rows, num_columns)` (HW), or
            `(num_channels, num_rows, num_columns)` (CHW).
        image2: An image Tensor of shape
            `(num_rows, num_columns, num_channels)` (HWC), or
            `(num_rows, num_columns)` (HW), or
            `(num_channels, num_rows, num_columns)`.
        factor: A floating point value or Tensor of type `tf.float32` above 0.0.
  
      Returns:
        A blended image Tensor of `tf.float32`.
  
      """
      with tf.name_scope("blend"):
          if factor == 0.0:
              return tf.convert_to_tensor(image1)
          if factor == 1.0:
              return tf.convert_to_tensor(image2)
  
          image1 = tf.cast(image1, dtype=tf.dtypes.float32)
          image2 = tf.cast(image2, dtype=tf.dtypes.float32)
  
          difference = image2 - image1
          scaled = factor * difference
  
          # Do addition in float.
          temp = image1 + scaled
  
          # Interpolate
          if factor > 0.0 and factor < 1.0:
              # Interpolation means we always stay within 0 and 255.
              temp = tf.round(temp)
              return temp
  
          # Extrapolate:
          #
          # We need to clip and then cast.
          temp = tf.round(tf.clip_by_value(temp, 0.0, 255.0))
          return temp
  
label_desc: Use image node_id 1 for calling a global function with example usage: blend(image1, image2, factor) and returns: temp, tf, tf, temp