class_docstr: Learner for an importanced-weighted advantage actor-critic.
docstr: Does a step of SGD and logs the results.
docstr_tok: ["Does","a","step","of","SGD","and","logs","the","results","."]
code: def step(self):
      """Does a step of SGD and logs the results."""
  
      # Do a batch of SGD.
      results = self._step()
  
      # Compute elapsed time.
      timestamp = time.time()
      elapsed_time = (
          timestamp - self._timestamp if self._timestamp else 0
      )
      self._timestamp = timestamp
  
      # Update our counts and record it.
      counts = self._counter.increment(steps=1, walltime=elapsed_time)
      results.update(counts)
  
      # Snapshot and attempt to write logs.
      self._snapshotter.save()
      self._logger.write(results)
  
imports: import time
  from typing import Dict, List, Mapping, Optional
  import acme
  from acme import specs
  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
  import tensorflow_probability
  import tree
  import trfl
label_desc: Use image node_id 4 for calling the IMPALALearner obj's underlying member method code with example usage: obj.step() without return types