class_docstr: Distributional DQN learner.
class_docstr_tok: ["Distributional","DQN","learner","."]
code: def step(self):
      # Do a batch of SGD.
      result = self._step()
  
      # Update our counts and record it.
      counts = self._counter.increment(steps=1)
      result.update(counts)
  
      # Checkpoint and attempt to write logs.
      if self._checkpointer is not None:
          self._checkpointer.save()
      if self._snapshotter is not None:
          self._snapshotter.save()
      self._logger.write(result)
  
imports: from typing import Dict, List, Optional, Tuple
  from acme import core
  from acme import types
  from acme.adders import reverb
  from acme.tf import losses
  from acme.tf import networks
  from acme.tf import savers
  from acme.tf import utils
  from acme.utils import counting
  from acme.utils import loggers
  import numpy
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 3 for calling the IQNLearner obj's underlying member method code with example usage: obj.step() without return types