class_docstr: D4PG learner.
  
  This is the learning component of a D4PG agent. IE it takes a dataset as input
  and implements update functionality to learn from this dataset.
class_docstr_tok: ["D4PG","learner",".","This","is","the","learning","component","of","a","D4PG","agent",".","IE","it","takes","a","dataset","as","input","and","implements","update","functionality","to","learn","from","this","dataset","."]
code: def step(self):
      # Run the learning step.
      fetches = self._replicated_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)
      fetches.update(counts)
  
      # Checkpoint and attempt to write the logs.
      if self._checkpointer is not None:
          self._checkpointer.save()
      if self._snapshotter is not None:
          self._snapshotter.save()
      self._logger.write(fetches)
  
imports: import time
  from typing import Dict, Iterator, List, Optional, Union, Sequence
  import acme
  from acme import types
  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
  import tree
label_desc: Use image node_id 4 for calling the D4PGLearner obj's underlying member method code with example usage: obj.step() without return types