code: def smooth_l1(sigma=3.0):
      """Create a smooth L1 loss functor.
  
      Args
          sigma: This argument defines the point where the loss changes from L2 to L1.
  
      Returns
          A functor for computing the smooth L1 loss given target data and predicted data.
      """
      sigma_squared = sigma**2
  
      def _smooth_l1(y_true, y_pred):
          """Compute the smooth L1 loss of y_pred w.r.t. y_true.
  
          Args
              y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
              y_pred: Tensor from the network of shape (B, N, 4).
  
          Returns
              The smooth L1 loss of y_pred w.r.t. y_true.
          """
          # separate target and state
          regression = y_pred
          regression_target = y_true[:, :, :-1]
          anchor_state = y_true[:, :, -1]
  
          # filter out "ignore" anchors
          indices = tensorflow.where(
              keras.backend.equal(anchor_state, 1)
          )
          regression = tensorflow.gather_nd(regression, indices)
          regression_target = tensorflow.gather_nd(
              regression_target, indices
          )
  
          # compute smooth L1 loss
          # f(x) = 0.5 * (sigma * x)^2          if |x| < 1 / sigma / sigma
          #        |x| - 0.5 / sigma / sigma    otherwise
          regression_diff = regression - regression_target
          regression_diff = keras.backend.abs(regression_diff)
          regression_loss = tensorflow.where(
              keras.backend.less(regression_diff, 1.0 / sigma_squared),
              0.5
              * sigma_squared
              * keras.backend.pow(regression_diff, 2),
              regression_diff - 0.5 / sigma_squared,
          )
  
          # compute the normalizer: the number of positive anchors
          normalizer = keras.backend.maximum(
              1, keras.backend.shape(indices)[0]
          )
          normalizer = keras.backend.cast(
              normalizer, dtype=keras.backend.floatx()
          )
          return keras.backend.sum(regression_loss) / normalizer
  
      return _smooth_l1
  
label_desc: Use image node_id 2 for calling a global function with example usage: smooth_l1(sigma) and returns: _smooth_l1, unknown