code: def SmoothL1Loss():
      r"""Returns a layer that computes a weighted, smoothed L1 loss for one batch.
  
    The layer takes three inputs:
  
      - Model output from one batch, an ndarray of float-valued elements.
  
      - A batch of element-wise target values, which matches the shape of the
        model output.
  
      - A batch of weights, which matches the shape of the model output.
  
    The layer computes a "smooth" L1 loss (a.k.a. Huber loss), for model output
    float :math:`y_i` and target float :math:`t_i`:
  
    .. math::
        \text{output} = \left\{ \begin{array}{cl}
            \frac 1 2 (y_i - t_i)^2, & \text{if}\ |y_i - t_i| < 1, \\
            |y_i - t_i| - \frac 1 2, & \text{otherwise}.
        \end{array} \right.
  
    The layer returns a weighted average of these element-wise values.
    """
  
      def f(
          model_output, targets, weights
      ):  # pylint: disable=invalid-name
          shapes.assert_same_shape(model_output, targets)
          shapes.assert_same_shape(model_output, weights)
          l1_dist = jnp.abs(model_output - targets)
          smooth_dist = jnp.where(
              l1_dist < 1, 0.5 * l1_dist**2, l1_dist - 0.5
          )
          weighted_smooth_dist = weights * smooth_dist
          return jnp.sum(weighted_smooth_dist) / jnp.sum(weights)
  
      return base.Fn("SmoothL1Loss", f)
  
imports: from trax import fastmath
  from trax import shapes
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import core
label_desc: Use image node_id 17 for calling a global function with example usage: SmoothL1Loss() and returns: base, unknown