class_docstr: The :class:`TernaryDense` class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference.
  
  Note that, the bias vector would not be tenaried.
  
  Parameters
  ----------
  n_units : int
      The number of units of this layer.
  act : activation function
      The activation function of this layer, usually set to ``tf.act.sign`` or apply :class:`SignLayer` after :class:`BatchNormLayer`.
  use_gemm : boolean
      If True, use gemm instead of ``tf.matmul`` for inference. (TODO).
  W_init : initializer
      The initializer for the weight matrix.
  b_init : initializer or None
      The initializer for the bias vector. If None, skip biases.
  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 : None or str
      A unique layer name.
code: def forward(self, inputs):
      # W = tl.act.sign(W)    # dont update ...
      alpha = compute_alpha(self.W)
      W_ = ternary_operation(self.W)
      W_ = tf.multiply(alpha, W_)
      # W = tf.Variable(W)
  
      outputs = tf.matmul(inputs, W_)
      # self.outputs = xnor_gemm(self.inputs, W) # TODO
  
      if self.b_init is not None:
          outputs = tf.nn.bias_add(outputs, self.b, name="bias_add")
      if self.act:
          outputs = self.act(outputs)
      return outputs
  
file_path: TensorLayer/tensorlayer/layers/dense/ternary_dense.py
imports: import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
  from tensorlayer.layers.utils import compute_alpha, ternary_operation
label_desc: Use image node_id 4 for calling the TernaryDense obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs