class_docstr: Implements Lion algorithm.
  
  Currently GPU-only.
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups.
      lr (float, optional): learning rate. (default: 1e-3)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square. (default: (0.9, 0.999))
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      set_grad_none (bool, optional): whether set grad to None when zero_grad()
          method is called. (default: True)
  
  .. _Symbolic Discovery of Optimization Algorithms:
      https://doi.org/10.48550/arXiv.2302.06675
code: def zero_grad(self):
      if self.set_grad_none:
          for group in self.param_groups:
              for p in group["params"]:
                  p.grad = None
      else:
          super(FusedLion, self).zero_grad()
  
file_path: deepspeed/deepspeed/ops/lion/fused_lion.py
imports: import torch
  from .multi_tensor_apply import MultiTensorApply
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import FusedLionBuilder
label_desc: Use image node_id 2 for calling the FusedLion obj's underlying member method code with example usage: obj.zero_grad() without return types