code: def clip_gradient(x, gradient_min: float, gradient_max: float):
      """Identity but the gradient in the backward pass is clipped.
  
      See "Human-level control through deep reinforcement learning" by Mnih et al,
      (https://www.nature.com/articles/nature14236)
  
      Note `grad(0.5 * clip_gradient(x)**2)` is equivalent to `grad(huber_loss(x))`.
  
      Note: x cannot be properly annotated because pytype does not support recursive
      types; we would otherwise use the chex.ArrayTree pytype annotation here. Most
      often x will be a single array of arbitrary shape, but the implementation
      supports pytrees.
  
      Args:
        x: a pytree of arbitrary shape.
        gradient_min: min elementwise size of the gradient.
        gradient_max: max elementwise size of the gradient.
  
      Returns:
        a vector of same shape of `x`.
      """
      chex.assert_type(x, float)
  
      def _compute_gradient(g):
          return (
              tree_map(
                  lambda g: jnp.clip(g, gradient_min, gradient_max), g
              ),
              0.0,
              0.0,
          )
  
      return x, _compute_gradient
  
imports: import chex
  import jax
  import jax.numpy
  from jax.tree_util import tree_map
label_desc: Use image node_id 2 for calling a global function with example usage: clip_gradient(x, gradient_min, gradient_max) and returns: x,  _compute_gradient, tree_map,  int,  int