code: def __init__(
      self,
      params=None,
      lr=0.01,
      lr_decay=0,
      weight_decay=0,
      initial_accumulator_value=0,
      eps=1e-10,
      foreach=None,
  ):
      TorchOptimizer.__init__(self)
      self.param_dict["lr"] = lr
      self.param_dict["lr_decay"] = lr_decay
      self.param_dict["weight_decay"] = weight_decay
      self.param_dict[
          "initial_accumulator_value"
      ] = initial_accumulator_value
      self.param_dict["eps"] = eps
      self.param_dict["foreach"] = foreach
      self.torch_class = type(self).__bases__[0]
  
      if params is None:
          return
  
      params = self.check_params(params)
  
      self.torch_class.__init__(self, params, **self.param_dict)
  
imports: from torch import optim
  from fate.components.components.nn.torch.base import TorchOptimizer
label_desc: Use image node_id 1 to create a new Adagrad object from inherited base classes: optim, TorchOptimizer with example: obj = Adagrad(params, lr, lr_decay, weight_decay, initial_accumulator_value, eps, foreach)