class_docstr: LANS Optimizer.
  
  Referenced from 'Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes'
  (http://arxiv.org/abs/2006.13484)
  
  Parameters
  ----------
  learning_rate : float, default 0.001
      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.
  beta1 : float, default 0.9
      Exponential decay rate for the first moment estimates.
  beta2 : float, default 0.999
      Exponential decay rate for the second moment estimates.
  epsilon : float, default 1e-6
      Small value to avoid division by 0.
  lower_bound : float, default None
      Lower limit of norm of weight
  upper_bound : float, default None
      Upper limit of norm of weight
  aggregate_num : int, default 4
      Number of weights to be aggregated in a list.
      They are passed to the optimizer for a single optimization step.
      In default, all the weights are aggregated.
  use_fused_step : bool, default True
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
docstr: Override update_multi_precision.
          
code: def update_multi_precision(self, indices, weights, grads, states):
      """Override update_multi_precision."""
      if self.use_fused_step:
          self.update(indices, weights, grads, states)
      else:
          super(LANS, self).update_multi_precision(
              indices, weights, grads, states
          )
  
file_path: mxnet/python/mxnet/optimizer/lans.py
imports: from __future__ import absolute_import
  import numpy
  from ..ndarray import zeros, clip, sqrt, where, square, ones_like, maximum, minimum
  from ..ndarray.contrib import multi_lans_update, multi_mp_lans_update
  from .optimizer import Optimizer, register
label_desc: Use image node_id 5 for calling the LANS obj's underlying member method code with example usage: obj.update_multi_precision(indices, weights, grads, states) without return types