code: def huber_loss(x: Array, delta: float = 1.0) -> Array:
      """Huber loss, similar to L2 loss close to zero, L1 loss away from zero.
  
      See "Robust Estimation of a Location Parameter" by Huber.
      (https://projecteuclid.org/download/pdf_1/euclid.aoms/1177703732).
  
      Args:
        x: a vector of arbitrary shape.
        delta: the bounds for the huber loss transformation, defaults at 1.
  
      Note `grad(huber_loss(x))` is equivalent to `grad(0.5 * clip_gradient(x)**2)`.
  
      Returns:
        a vector of same shape of `x`.
      """
      chex.assert_type(x, float)
  
      # 0.5 * x^2                  if |x| <= d
      # 0.5 * d^2 + d * (|x| - d)  if |x| > d
      abs_x = jnp.abs(x)
      quadratic = jnp.minimum(abs_x, delta)
      # Same as max(abs_x - delta, 0) but avoids potentially doubling gradient.
      linear = abs_x - quadratic
      return 0.5 * quadratic**2 + delta * linear
  
imports: import chex
  import jax
  import jax.numpy
  from jax.tree_util import tree_map
label_desc: Use image node_id 1 for calling a global function with example usage: huber_loss(x, delta) and returns: unknown