code: def ranger(
      params: Tensor | Iterable,  # Model parameters
      lr: float | slice,  # Default learning rate
      mom: float = 0.95,  # Gradient moving average (β1) coefficient
      wd: Real = 0.01,  # Optional weight decay (true or L2)
      eps: float = 1e-6,  # Added for numerical stability
      k: int = 6,  # How often to conduct Lookahead step
      alpha: float = 0.5,  # Slow weight moving average coefficient
      **kwargs,
  ) -> Lookahead:
      "Convenience method for `Lookahead` with `RAdam`"
      return Lookahead(
          RAdam(params, lr=lr, mom=mom, wd=wd, eps=eps, **kwargs),
          k=k,
          alpha=alpha,
      )
  
imports: from __future__ import annotations
  from .torch_basics import *
label_desc: Use image node_id 24 for calling a global function with example usage: ranger(params, lr, mom, wd, eps, k, alpha) and returns: Lookahead