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: 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: 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: epsilon kludge to help with numerical stability while calculating adaptive_lr
class_docstr_tok: ["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",":","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",":","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",":","epsilon","kludge","to","help","with","numerical","stability","while","calculating","adaptive_lr"]
code: def zero_grad(self):
      self.optim.zero_grad()
  
file_path: SlowFast/slowfast/models/optimizer.py
imports: import torch
  import slowfast.utils.lr_policy
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