code: def testTrainWithSparseTensorAndDenseFeaturesLayer(self, agent_class):
      obs_spec = {
          "dense": tensor_spec.BoundedTensorSpec(
              dtype=tf.float32, shape=[3], minimum=-10.0, maximum=10.0
          ),
          "sparse_terms": tf.SparseTensorSpec(
              dtype=tf.string, shape=[4]
          ),
          "sparse_frequencies": tf.SparseTensorSpec(
              dtype=tf.float32, shape=[4]
          ),
      }
      cat_column = tf.compat.v2.feature_column.categorical_column_with_hash_bucket(
          "sparse_terms", hash_bucket_size=5
      )
      weighted_cat_column = (
          tf.compat.v2.feature_column.weighted_categorical_column(
              cat_column, weight_feature_key="sparse_frequencies"
          )
      )
      feature_columns = [
          tf.compat.v2.feature_column.numeric_column("dense", [3]),
          tf.compat.v2.feature_column.embedding_column(
              weighted_cat_column, 3
          ),
      ]
      dense_features_layer = tf.compat.v2.keras.layers.DenseFeatures(
          feature_columns
      )
      time_step_spec = ts.time_step_spec(obs_spec)
      q_net = q_network.QNetwork(
          time_step_spec.observation,
          self._action_spec,
          preprocessing_combiner=dense_features_layer,
      )
      agent = agent_class(
          time_step_spec,
          self._action_spec,
          q_network=q_net,
          optimizer=tf.compat.v1.train.AdamOptimizer(),
      )
  
      observations = tensor_spec.sample_spec_nest(
          obs_spec, outer_dims=[5, 2]
      )
      # sparse_terms and sparse_frequencies must be defined on matching indices.
      observations["sparse_terms"] = tf.SparseTensor(
          indices=observations["sparse_frequencies"].indices,
          values=tf.as_string(
              tf.math.round(observations["sparse_frequencies"].values)
          ),
          dense_shape=observations["sparse_frequencies"].dense_shape,
      )
      if not tf.executing_eagerly():
          # Mimic unknown inner dims on the SparseTensor
          def _unknown_inner_shape(t):
              if not isinstance(t, tf.SparseTensor):
                  return t
              return tf.SparseTensor(
                  indices=t.indices,
                  values=t.values,
                  dense_shape=tf.compat.v1.placeholder_with_default(
                      t.dense_shape, shape=t.dense_shape.shape
                  ),
              )
  
          observations = tf.nest.map_structure(
              _unknown_inner_shape, observations
          )
          self.assertIsNone(
              tf.get_static_value(
                  observations["sparse_terms"].dense_shape
              )
          )
  
      time_step = ts.restart(observations, batch_size=[5, 2])
      action_step = tensor_spec.sample_spec_nest(
          self._action_spec, outer_dims=[5, 2]
      )
      p_step = policy_step.PolicyStep(
          action=action_step, state=(), info=()
      )
      traj = trajectory.from_transition(time_step, p_step, time_step)
      loss_info = agent.train(traj)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      loss_info = self.evaluate(loss_info)
      self.assertGreater(loss_info.loss, 0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.networks import sequential
  from tf_agents.networks import test_utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import test_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 17 for calling the DqnAgentTest obj's underlying member method code with example usage: obj.testTrainWithSparseTensorAndDenseFeaturesLayer(agent_class) and returns: tf, t