class_docstr: An Acme learner based around SGD on batches.
  
  This learner currently supports optional prioritized replay and assumes a
  TrainingState as described above.
code: def save(self) -> TrainingState:
      # Serialize only the first replica of parameters and optimizer state.
      return utils.get_from_first_device(self._state)
  
imports: import functools
  import time
  from typing import Dict, Iterator, List, NamedTuple, Optional, Tuple
  import acme
  from acme.adders import reverb
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import async_utils
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import jax.numpy
  import optax
  import reverb
  import tree
  import typing_extensions
label_desc: Use image node_id 4 for calling the SGDLearner obj's underlying member method code with example usage: obj.save() and returns: utils