class_docstr: The AdamW optimizer.
  
  This class implements the optimizer described in *Decoupled Weight Decay Regularization*,
   available at https://arxiv.org/pdf/1711.05101.pdf.
  
  Updates are applied by::
  
      grad = clip(grad * rescale_grad, clip_gradient)
      m = beta1 * m + (1 - beta1) * grad
      v = beta2 * v + (1 - beta2) * (grad**2)
      lr = learning_rate * sqrt(1 - beta2**t) / (1 - beta1**t)
      w = w - lr * (m / (sqrt(v) + epsilon) + wd * w)
  
  
  Also, we can turn off the bias correction term and the updates are as follows::
  
      grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
      m = beta1 * m + (1 - beta1) * grad
      v = beta2 * v + (1 - beta2) * (grad**2)
      lr = learning_rate
      w = w - lr * (m / (sqrt(v) + epsilon) + wd * w)
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  
  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.
  correct_bias : bool, default True
     Can be set to False to avoid correcting bias in Adam (e.g. like in Bert TF repository).
     Default True.
  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: state creation function.
code: def create_state(self, index, weight):
      """state creation function."""
      return (
          zeros(
              weight.shape, weight.context, dtype=weight.dtype
          ),  # mean
          zeros(weight.shape, weight.context, dtype=weight.dtype),
      )
  
file_path: mxnet/python/mxnet/optimizer/adamW.py
imports: import math
  import os
  import numpy
  from .optimizer import Optimizer, register
  from ..ndarray import zeros, clip, sqrt, square, full, NDArray
  from ..ndarray.contrib import mp_adamw_update, adamw_update, multi_mp_adamw_update, multi_adamw_update
label_desc: Use image node_id 2 for calling the AdamW obj's underlying member method code with example usage: obj.create_state(index, weight) and returns: zeros,  zeros