code: def RAdam(
      learn_rate: FloatOrSeq = ADAM_DEFAULTS["learn_rate"],
      *,
      beta1: FloatOrSeq = ADAM_DEFAULTS["beta1"],
      beta2: FloatOrSeq = ADAM_DEFAULTS["beta2"],
      eps: FloatOrSeq = ADAM_DEFAULTS["eps"],
      L2: FloatOrSeq = ADAM_DEFAULTS["L2"],
      L2_is_weight_decay: bool = cast(
          bool, ADAM_DEFAULTS["L2_is_weight_decay"]
      ),
      grad_clip: FloatOrSeq = ADAM_DEFAULTS["grad_clip"],
      use_averages: bool = True,
  ):
      return Optimizer(
          learn_rate,
          beta1=beta1,
          beta2=beta2,
          eps=eps,
          grad_clip=grad_clip,
          L2_is_weight_decay=L2_is_weight_decay,
          L2=L2,
          use_averages=use_averages,
          use_radam=True,
      )
  
file_path: thinc/thinc/optimizers.py
imports: import math
  from collections import defaultdict
  from typing import Dict, List, Optional, Tuple, Union, cast
  from .backends import get_array_ops
  from .config import registry
  from .types import FloatsXd, Generator
label_desc: Use image node_id 1 for calling a global function with example usage: RAdam(learn_rate) and returns: Optimizer