def lars(
    params: List[Tensor],
    params2: List[Tensor],
    d_p_list: List[Tensor],
    momentum_buffer_list: List[Optional[Tensor]],
    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
    has_sparse_grad: bool = None,
    foreach: bool = None,
    *,
    eeta: float,
    eps: float,
    weight_decay: float,
    momentum: float,
    lr: float,
    dampening: float,
    nesterov: bool,
    maximize: bool,
    fused: bool
):
    r"""Functional API that performs LARS algorithm computation.
    dampening = 0
    nesterov = False
    maximize = False
    """

    if foreach is None:
        # Placeholder for more complex foreach logic to be added when value is not set
        foreach = False

    if foreach and torch.jit.is_scripting():
        raise RuntimeError("torch.jit.script not supported with foreach optimizers")

    func = _single_tensor_lars

    func(
        params,
        params2,
        d_p_list,
        momentum_buffer_list,
        eeta=eeta,
        eps=eps,
        weight_decay=weight_decay,
        momentum=momentum,
        lr=lr,
        dampening=dampening,
        nesterov=nesterov,
        has_sparse_grad=has_sparse_grad,
        maximize=maximize,
        fused=fused,
    )
Functional API that performs LARS algorithm computation.
    dampening = 0
    nesterov = False
    maximize = False
    
intel_extension_for_pytorch/optim/_functional.py