class_docstr: The :class:`QuanDenseWithBN` class is a quantized fully connected layer with BN, which weights are 'bitW' bits and the output of the previous layer
  are 'bitA' bits while inferencing.
  
  Parameters
  ----------
  n_units : int
      The number of units of this layer.
  act : activation function
      The activation function of this layer.
  decay : float
      A decay factor for `ExponentialMovingAverage`.
      Suggest to use a large value for large dataset.
  epsilon : float
      Eplison.
  is_train : boolean
      Is being used for training or inference.
  beta_init : initializer or None
      The initializer for initializing beta, if None, skip beta.
      Usually you should not skip beta unless you know what happened.
  gamma_init : initializer or None
      The initializer for initializing gamma, if None, skip gamma.
  bitW : int
      The bits of this layer's parameter
  bitA : int
      The bits of the output of previous layer
  use_gemm : boolean
      If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO).
  W_init : initializer
      The initializer for the the weight matrix.
  W_init_args : dictionary
      The arguments for the weight matrix initializer.
  in_channels: int
      The number of channels of the previous layer.
      If None, it will be automatically detected when the layer is forwarded for the first time.
  name : a str
      A unique layer name.
  
  Examples
  ---------
  >>> import tensorlayer as tl
  >>> net = tl.layers.Input([50, 256])
  >>> layer = tl.layers.QuanDenseWithBN(128, act='relu', name='qdbn1')(net)
  >>> print(layer)
  >>> net = tl.layers.QuanDenseWithBN(256, act='relu', name='qdbn2')(net)
  >>> print(net)
code: def _w_fold(self, w, gama, var, epsilon):
      return tf.compat.v1.div(
          tf.multiply(gama, w), tf.sqrt(var + epsilon)
      )
  
file_path: TensorLayer/tensorlayer/layers/dense/quan_dense_bn.py
imports: import tensorflow
  from tensorflow.python.training import moving_averages
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
  from tensorlayer.layers.utils import quantize_active_overflow, quantize_weight_overflow
label_desc: Use image node_id 6 for calling the QuanDenseWithBN obj's underlying member method code with example usage: obj._w_fold(w, gama, var, epsilon) and returns: tf