code: def RAdam(
      params: Tensor | Iterable,  # Model parameters
      lr: float | slice,  # Default learning rate
      mom: float = 0.9,  # Gradient moving average (β1) coefficient
      sqr_mom: float = 0.99,  # Gradient squared moving average (β2) coefficient
      eps: float = 1e-5,  # Added for numerical stability
      wd: Real = 0.0,  # Optional weight decay (true or L2)
      beta: float = 0.0,  # Set to enable SAdam
      decouple_wd: bool = True,  # Apply true weight decay (RAdamW) or L2 regularization (RAdam)
  ) -> Optimizer:
      "A RAdam/RAdamW `Optimizer`"
      cbs = [weight_decay] if decouple_wd else [l2_reg]
      cbs += [
          partial(average_grad, dampening=True),
          average_sqr_grad,
          step_stat,
          radam_step,
      ]
      return Optimizer(
          params,
          cbs,
          lr=lr,
          mom=mom,
          sqr_mom=sqr_mom,
          eps=eps,
          wd=wd,
          beta=beta,
      )
  
imports: from __future__ import annotations
  from .torch_basics import *
label_desc: Use image node_id 16 for calling a global function with example usage: RAdam(params, lr, mom, sqr_mom, eps, wd, beta, decouple_wd) and returns: Optimizer