class_docstr: Synchronization optimizer, which updates variables periodically to the value of a corresponding
  set of source variables (specification key: `synchronization`).
  
  Args:
      optimizer (specification): Optimizer configuration
          (<span style="color:#C00000"><b>required</b></span>).
      update_weight (parameter, 0.0 < float <= 1.0): Update weight
          (<span style="color:#C00000"><b>required</b></span>).
      sync_frequency (parameter, int >= 1): Interval between updates which also perform a
          synchronization step (<span style="color:#00C000"><b>default</b></span>: every update).
      name (string): (<span style="color:#0000C0"><b>internal use</b></span>).
      arguments_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      *,
      update_weight,
      sync_frequency=None,
      name=None,
      arguments_spec=None,
  ):
      super().__init__(name=name, arguments_spec=arguments_spec)
  
      self.update_weight = self.submodule(
          name="update_weight",
          module=update_weight,
          modules=parameter_modules,
          dtype="float",
          min_value=0.0,
          max_value=1.0,
      )
  
      if sync_frequency is None:
          sync_frequency = 1
      self.sync_frequency = self.submodule(
          name="sync_frequency",
          module=sync_frequency,
          modules=parameter_modules,
          dtype="int",
          min_value=1,
      )
  
imports: import tensorflow
  from tensorforce.core import parameter_modules, TensorSpec, tf_function, tf_util
  from tensorforce.core.optimizers import Optimizer
label_desc: Use image node_id 1 to create a new Synchronization object from inherited base classes: Optimizer with example: obj = Synchronization()