docstr: Add a param group to the :class:`Optimizer` s `param_groups`.
  
  This can be useful when fine tuning a pre-trained network as frozen
  layers can be made trainable and added to the :class:`Optimizer` as
  training progresses.
  
  Args:
      param_group (dict): Specifies what Tensors should be optimized along
      with group specific optimization options.
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code: def add_param_group(self, param_group):
      r"""Add a param group to the :class:`Optimizer` s `param_groups`.
  
      This can be useful when fine tuning a pre-trained network as frozen
      layers can be made trainable and added to the :class:`Optimizer` as
      training progresses.
  
      Args:
          param_group (dict): Specifies what Tensors should be optimized along
          with group specific optimization options.
      """
      param_group["n_avg"] = 0
      param_group["step_counter"] = 0
      self.optimizer.add_param_group(param_group)
  
imports: import warnings
  from collections import defaultdict
  from typing import Optional, Union
  import torch
  from pytext.config.component import create_optimizer
  from pytext.optimizer.lamb import Lamb
  from pytext.optimizer.madgrad import MADGRAD
  from pytext.optimizer.optimizers import Adagrad, Adam, AdamW, Optimizer, SGD
  from pytext.optimizer.radam import RAdam
  from torch.optim import Optimizer
label_desc: Use image node_id 10 for calling the StochasticWeightAveraging obj's underlying member method code with example usage: obj.add_param_group(param_group) without return types