code: def dpg(
      q_max,
      a_max,
      dqda_clipping=None,
      clip_norm=False,
      name="DpgLearning",
  ):
      """Implements the Deterministic Policy Gradient (DPG) loss as a TensorFlow Op.
  
      This op implements the loss for the `actor`, the `critic` can instead be
      updated by minimizing the `value_ops.td_learning` loss.
  
      See "Deterministic Policy Gradient Algorithms" by Silver, Lever, Heess,
      Degris, Wierstra, Riedmiller (http://proceedings.mlr.press/v32/silver14.pdf).
  
      Args:
        q_max: Tensor holding Q-values generated by Q network with the input of
          (state, a_max) pair, shape `[B]`.
        a_max: Tensor holding the optimal action, shape `[B, action_dimension]`.
        dqda_clipping: `int` or `float`, clips the gradient dqda element-wise
          between `[-dqda_clipping, dqda_clipping]`.
        clip_norm: Whether to perform dqda clipping on the vector norm of the last
          dimension, or component wise (default).
        name: name to prefix ops created within this op.
  
      Returns:
        A namedtuple with fields:
  
        * `loss`: a tensor containing the batch of losses, shape `[B]`.
        * `extra`: a namedtuple with fields:
            * `q_max`: Tensor holding the optimal Q values, `[B]`.
            * `a_max`: Tensor holding the optimal action, `[B, action_dimension]`.
            * `dqda`: Tensor holding the derivative dq/da, `[B, action_dimension]`.
  
      Raises:
        ValueError: If `q_max` doesn't depend on `a_max` or if `dqda_clipping <= 0`.
      """
  
      # DPG op.
      with tf.name_scope(name, values=[q_max, a_max]):
          # Calculate the gradient dq/da.
          dqda = tf.gradients([q_max], [a_max])[0]
  
          # Check that `q_max` depends on `a_max`.
          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
          )
          return base_ops.LossOutput(
              loss, DPGExtra(q_max=q_max, a_max=a_max, dqda=dqda)
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import tensorflow.compat.v1
  from trfl import base_ops
label_desc: Use image node_id 1 for calling a global function with example usage: dpg(q_max, a_max, dqda_clipping, clip_norm, name) and returns: base_ops