code: def test_full_learner(self):
      # Create dataset.
      environment = fakes.DiscreteEnvironment(
          num_actions=5,
          num_observations=10,
          obs_dtype=np.float32,
          episode_length=10,
      )
      spec = specs.make_environment_spec(environment)
      dataset = fakes.transition_dataset(environment).batch(
          2, drop_remainder=True
      )
  
      # Build network.
      network = networks.IQNNetwork(
          torso=_make_torso_network(num_outputs=2),
          head=_make_head_network(num_outputs=spec.actions.num_values),
          latent_dim=2,
          num_quantile_samples=1,
      )
      tf2_utils.create_variables(network, [spec.observations])
  
      # Build learner.
      counter = counting.Counter()
      learner = iqn.IQNLearner(
          network=network,
          target_network=copy.deepcopy(network),
          dataset=dataset,
          learning_rate=1e-4,
          discount=0.99,
          importance_sampling_exponent=0.2,
          target_update_period=1,
          counter=counter,
      )
  
      # Run a learner step.
      learner.step()
  
      # Check counts from IQN learner.
      counts = counter.get_counts()
      self.assertEqual(1, counts["steps"])
  
      # Check learner state.
      self.assertEqual(1, learner.state["num_steps"].numpy())
  
file_path: acme/acme/agents/tf/iqn/learning_test.py
imports: import copy
  from acme import specs
  from acme.agents.tf import iqn
  from acme.testing import fakes
  from acme.tf import networks
  from acme.tf import utils
  from acme.utils import counting
  import numpy
  import sonnet
  from absl.testing import absltest
label_desc: Use image node_id 1 for calling the IQNLearnerTest obj's underlying member method code with example usage: obj.test_full_learner() without return types