class_docstr: LARS for PyTorch
  
  Paper: `Large batch training of Convolutional Networks` - https://arxiv.org/pdf/1708.03888.pdf
  Args:
      params (iterable): iterable of parameters to optimize or dicts defining parameter groups.
      lr (float, optional): learning rate (default: 1.0).
      momentum (float, optional): momentum factor (default: 0)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      dampening (float, optional): dampening for momentum (default: 0)
      nesterov (bool, optional): enables Nesterov momentum (default: False)
      trust_coeff (float): trust coefficient for computing adaptive lr / trust_ratio (default: 0.001)
      eps (float): eps for division denominator (default: 1e-8)
      trust_clip (bool): enable LARC trust ratio clipping (default: False)
      always_adapt (bool): always apply LARS LR adapt, otherwise only when group weight_decay != 0 (default: False)
code: def __setstate__(self, state):
      super().__setstate__(state)
      for group in self.param_groups:
          group.setdefault("nesterov", False)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 2 for calling the Lars obj's underlying member method code with example usage: obj.__setstate__(state) without return types