class_docstr: Implement multiple optimizers needed for sparse adam
class_docstr_tok: ["Implement","multiple","optimizers","needed","for","sparse","adam"]
code: def param_groups(self):
      param_groups = []
      for optimizer in self.optimizers:
          param_groups.extend(optimizer.param_groups)
      return param_groups
  
imports: import torch
  import torch.optim
  from torch.nn.utils import clip_grad_norm_
  import operator
  import functools
  from copy import copy
  from math import sqrt
  import types
  import os
  import importlib
  from onmt.utils.misc import fn_args
label_desc: Use image node_id 2 for calling the MultipleOptimizer obj's underlying member method code with example usage: obj.param_groups() and returns: param_groups