class_docstr: Learner for an importanced-weighted advantage actor-critic.
class_docstr_tok: ["Learner","for","an","importanced-weighted","advantage","actor-critic","."]
code: def get_variables(
      self, names: Sequence[str]
  ) -> List[networks_lib.Params]:
      # Return first replica of parameters.
      return utils.get_from_first_device(
          [self._state.params], as_numpy=False
      )
  
imports: import time
  from typing import Dict, Iterator, List, NamedTuple, Optional, Sequence, Tuple
  from absl import logging
  import acme
  from acme.agents.jax.impala import networks
  from acme.jax import losses
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import jax.numpy
  import numpy
  import optax
  import reverb
label_desc: Use image node_id 3 for calling the IMPALALearner obj's underlying member method code with example usage: obj.get_variables(names) and returns: utils