code: def sg_dense(tensor, opt):
      r
      # parameter initialize
      w = tf.sg_initializer.he_uniform('W', (opt.in_dim, opt.dim),
                                       regularizer=opt.regularizer, summary=opt.summary)
      b = tf.sg_initializer.constant('b', opt.dim, summary=opt.summary) if opt.bias else 0
  
      # apply transform
      out = tf.matmul(tensor, w) + b
  
      return out
docs: r"""Applies a full connection.
      
      Args:
        tensor: A 2-D tensor (automatically passed by decorator).
        opt:
          in_dim: An `integer`. The size of input dimension.
          dim: An `integer`. The size of output dimension.
          bias: Boolean. If True, biases are added.
          regularizer:  A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
            will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
          summary: If True, summaries are added. The default is True.
  
        
      Returns:
        A `Tensor` with the same type as `tensor`.