code: def invert(image, signed_float=False):
      """Invert an image.
  
      Invert the intensity range of the input image, so that the dtype maximum
      is now the dtype minimum, and vice-versa. This operation is
      slightly different depending on the input dtype:
  
      - unsigned integers: subtract the image from the dtype maximum
      - signed integers: subtract the image from -1 (see Notes)
      - floats: subtract the image from 1 (if signed_float is False, so we
        assume the image is unsigned), or from 0 (if signed_float is True).
  
      See the examples for clarification.
  
      Parameters
      ----------
      image : ndarray
          Input image.
      signed_float : bool, optional
          If True and the image is of type float, the range is assumed to
          be [-1, 1]. If False and the image is of type float, the range is
          assumed to be [0, 1].
  
      Returns
      -------
      inverted : ndarray
          Inverted image.
  
      Notes
      -----
      Ideally, for signed integers we would simply multiply by -1. However,
      signed integer ranges are asymmetric. For example, for np.int8, the range
      of possible values is [-128, 127], so that -128 * -1 equals -128! By
      subtracting from -1, we correctly map the maximum dtype value to the
      minimum.
  
      Examples
      --------
      >>> img = np.array([[100,  0, 200],
      ...                 [  0, 50,   0],
      ...                 [ 30,  0, 255]], np.uint8)
      >>> invert(img)
      array([[155, 255,  55],
             [255, 205, 255],
             [225, 255,   0]], dtype=uint8)
      >>> img2 = np.array([[ -2, 0, -128],
      ...                  [127, 0,    5]], np.int8)
      >>> invert(img2)
      array([[   1,   -1,  127],
             [-128,   -1,   -6]], dtype=int8)
      >>> img3 = np.array([[ 0., 1., 0.5, 0.75]])
      >>> invert(img3)
      array([[1.  , 0.  , 0.5 , 0.25]])
      >>> img4 = np.array([[ 0., 1., -1., -0.25]])
      >>> invert(img4, signed_float=True)
      array([[-0.  , -1.  ,  1.  ,  0.25]])
      """
      if image.dtype == "bool":
          inverted = ~image
      elif np.issubdtype(image.dtype, np.unsignedinteger):
          max_val = dtype_limits(image, clip_negative=False)[1]
          inverted = np.subtract(max_val, image, dtype=image.dtype)
      elif np.issubdtype(image.dtype, np.signedinteger):
          inverted = np.subtract(-1, image, dtype=image.dtype)
      else:  # float dtype
          if signed_float:
              inverted = -image
          else:
              inverted = np.subtract(1, image, dtype=image.dtype)
      return inverted
  
label_desc: Use image node_id 1 for calling a global function with example usage: invert(image, signed_float) and returns: inverted