class_docstr: The DCASGD optimizer.
  
  This class implements the optimizer described in *Asynchronous Stochastic Gradient Descent
  with Delay Compensation for Distributed Deep Learning*,
  available at https://arxiv.org/abs/1609.08326.
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  Parameters
  ----------
  learning_rate : float, default 0.1
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  momentum : float, optional
     The momentum value.
  lamda : float, optional
     Scale DC value.
  use_fused_step : bool, default False
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
code: def __init__(
      self,
      learning_rate=0.1,
      momentum=0.0,
      lamda=0.04,
      use_fused_step=False,
      **kwargs,
  ):
      super(DCASGD, self).__init__(
          learning_rate=learning_rate,
          use_fused_step=use_fused_step,
          **kwargs,
      )
      self.momentum = momentum
      self.weight_previous = {}
      self.lamda = lamda
  
imports: from __future__ import absolute_import
  from ..ndarray import zeros, clip, square
  from .optimizer import Optimizer, register
label_desc: Use image node_id 1 to create a new DCASGD object from inherited base classes: Optimizer with example: obj = DCASGD(learning_rate, momentum, lamda, use_fused_step)