code: def call(self, inputs, step_type=(), network_state=()):
      _, action_nested = inputs
  
      flat_action = tf.nest.flatten(action_nested)
  
      val = self._get_proximity_to_anchor(
          flat_action[self.idx_continuous],
          self._anchor_point,
          self._dist,
      )
      res = tf.reduce_min(val, axis=-1)
  
      # If there are categorical actions, then make the Q value very high
      # when they have high returns in categorical_action_returns.
      # The expectation is that CEM will find this the best
      # possible action since this Q value dominates the ones coming from
      # proximity to the given anchor point.
      if self._sampler_type == "continuous_and_one_hot":
          cat_actions_dist_1 = tf.abs(
              flat_action[1 - self.idx_continuous] - 1
          )
          cat_returns = tf.constant(self._categorical_action_returns)
          overall_cat_actions_vals = -tf.multiply(
              cat_returns, tf.cast(cat_actions_dist_1, tf.float32)
          )
          res += tf.reduce_min(overall_cat_actions_vals, axis=1)
      elif self._sampler_type == "hybrid":
          val_discrete = self._get_proximity_to_anchor(
              flat_action[1 - self.idx_continuous],
              self._anchor_point,
              self._dist,
          )
          res += tf.reduce_min(val_discrete, axis=-1)
  
      return res, ()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import flags
  from absl import logging
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.networks import network
  from tf_agents.policies import qtopt_cem_policy
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler_continuous
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler_continuous_and_one_hot
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler_hybrid
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
label_desc: Use image node_id 2 for calling the DummyNet obj's underlying member method code with example usage: obj.call(inputs, step_type, network_state) and returns: res,  