class_docstr: This class is adapted from
  https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py to
  include ignoring LARS application specific parameters (e.g. 1D params)
  
  Args:
      optimizer (torch.optim): Pytorch optimizer to wrap and modify learning rate for.
      trust_coefficient: Trust coefficient for calculating the lr.
          See https://arxiv.org/abs/1708.03888
      clip (bool): Decides between clipping or scaling mode of LARS. If `clip=True` the
          learning rate is set to `min(optimizer_lr, local_lr)` for each parameter.
          If `clip=False` the learning rate is set to `local_lr*optimizer_lr`.
      eps (float): epsilon kludge to help with numerical stability while calculating
      adaptive_lr.
      ignore_1d_param (float): If true, does not update 1 dimentional parameters.
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code: def zero_grad(self):
      self.optim.zero_grad()
  
imports: from dataclasses import dataclass
  import torch
  from hydra.core.config_store import ConfigStore
  from omegaconf import MISSING
label_desc: Use image node_id 10 for calling the LARS obj's underlying member method code with example usage: obj.zero_grad() without return types