code: def dpg(
      q_max: tf.Tensor,
      a_max: tf.Tensor,
      tape: tf.GradientTape,
      dqda_clipping: Optional[float] = None,
      clip_norm: bool = False,
  ) -> tf.Tensor:
      """Deterministic policy gradient loss, similar to trfl.dpg."""
  
      # Calculate the gradient dq/da.
      dqda = tape.gradient([q_max], [a_max])[0]
  
      if dqda is None:
          raise ValueError("q_max needs to be a function of a_max.")
  
      # Clipping the gradient dq/da.
      if dqda_clipping is not None:
          if dqda_clipping <= 0:
              raise ValueError(
                  "dqda_clipping should be bigger than 0, {} found".format(
                      dqda_clipping
                  )
              )
          if clip_norm:
              dqda = tf.clip_by_norm(dqda, dqda_clipping, axes=-1)
          else:
              dqda = tf.clip_by_value(
                  dqda, -1.0 * dqda_clipping, dqda_clipping
              )
  
      # Target_a ensures correct gradient calculated during backprop.
      target_a = dqda + a_max
      # Stop the gradient going through Q network when backprop.
      target_a = tf.stop_gradient(target_a)
      # Gradient only go through actor network.
      loss = 0.5 * tf.reduce_sum(tf.square(target_a - a_max), axis=-1)
      # This recovers the DPG because (letting w be the actor network weights):
      # d(loss)/dw = 0.5 * (2 * (target_a - a_max) * d(target_a - a_max)/dw)
      #            = (target_a - a_max) * [d(target_a)/dw  - d(a_max)/dw]
      #            = dq/da * [d(target_a)/dw  - d(a_max)/dw]  # by defn of target_a
      #            = dq/da * [0 - d(a_max)/dw]                # by stop_gradient
      #            = - dq/da * da/dw
  
      return loss
  
label_desc: Use image node_id 1 for calling a global function with example usage: dpg(q_max, a_max, tape, dqda_clipping, clip_norm) and returns: loss