code: def Larc(
      params: Tensor | Iterable,  # Model parameters
      lr: float | slice,  # Default learning rate
      mom: float = 0.9,  # Gradient moving average (β1) coefficient
      clip: bool = True,  # LARC if clip=True, LARS if clip=False
      trust_coeff: float = 0.02,  # Trust coeffiecnet for calculating layerwise LR
      eps: float = 1e-8,  # Added for numerical stability
      wd: Real = 0.0,  # Optional weight decay (true or L2)
      decouple_wd: bool = True,  # Apply true weight decay or L2 regularization
  ) -> Optimizer:
      "A LARC/LARS `Optimizer`"
      cbs = [weight_decay] if decouple_wd else [l2_reg]
      if mom != 0.0:
          cbs.append(average_grad)
      cbs += [partial(larc_layer_lr, clip=clip), larc_step]
      return Optimizer(
          params,
          cbs,
          lr=lr,
          mom=mom,
          trust_coeff=trust_coeff,
          eps=eps,
          wd=wd,
      )
  
imports: from __future__ import annotations
  from .torch_basics import *
label_desc: Use image node_id 21 for calling a global function with example usage: Larc(params, lr, mom, clip, trust_coeff, eps, wd, decouple_wd) and returns: Optimizer