class_docstr: A JAX implementation of the SAC agent.
class_docstr_tok: ["A","JAX","implementation","of","the","SAC","agent","."]
docstr: Syncs the target weights with the online weights.
docstr_tok: ["Syncs","the","target","weights","with","the","online","weights","."]
code: def _maybe_sync_weights(self):
      """Syncs the target weights with the online weights."""
      if (
          self.target_update_type == "hard"
          and self.training_steps % self.target_update_period != 0
      ):
          return
  
      def _sync_weights(target_p, online_p):
          return (
              self.target_smoothing_coefficient * online_p
              + (1 - self.target_smoothing_coefficient) * target_p
          )
  
      self.target_params = jax.tree_map(
          _sync_weights, self.target_params, self.network_params
      )
  
imports: import functools
  import math
  import operator
  import time
  from typing import Any, Mapping, Tuple
  from absl import logging
  from dopamine.jax import continuous_networks
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  import dopamine.labs.sac_from_pixels.continuous_networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  import flax
  from flax import linen
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 4 for calling the SACAgent obj's underlying member method code with example usage: obj._maybe_sync_weights() and returns: unknown