code: def dpg_loss(
      a_t: Array,
      dqda_t: Array,
      dqda_clipping: Optional[Scalar] = None,
      use_stop_gradient: bool = True,
  ) -> Array:
      """Calculates the deterministic policy gradient (DPG) loss.
  
      See "Deterministic Policy Gradient Algorithms" by Silver, Lever, Heess,
      Degris, Wierstra, Riedmiller (http://proceedings.mlr.press/v32/silver14.pdf).
  
      Args:
        a_t: continuous-valued action at time t.
        dqda_t: gradient of Q(s,a) wrt. a, evaluated at time t.
        dqda_clipping: clips the gradient to have norm <= `dqda_clipping`.
        use_stop_gradient: bool indicating whether or not to apply stop gradient
          to targets.
  
      Returns:
        DPG loss.
      """
      chex.assert_rank([a_t, dqda_t], 1)
      chex.assert_type([a_t, dqda_t], float)
  
      if dqda_clipping is not None:
          dqda_t = _clip_by_l2_norm(dqda_t, dqda_clipping)
      target_tm1 = dqda_t + a_t
      target_tm1 = jax.lax.select(
          use_stop_gradient,
          jax.lax.stop_gradient(target_tm1),
          target_tm1,
      )
      return losses.l2_loss(target_tm1 - a_t)
  
file_path: rlax/rlax/_src/policy_gradients.py
imports: from typing import Optional, Tuple
  import chex
  import jax
  import jax.numpy
  from rlax._src import distributions
  from rlax._src import losses
label_desc: Use image node_id 2 for calling a global function with example usage: dpg_loss(a_t, dqda_t, dqda_clipping, use_stop_gradient) and returns: losses