code: def predict(self, x):
          
          self.x[1:] = x
          self.y = self.activation(np.sum(self.w*self.x, axis=1), f=self.f)
          return self.y
docs: This function make forward pass through this layer (no update).
  
          **Args:**
  
          * `x` : input vector (1-dimensional array)
  
          **Returns:**
          
          * `y` : output of MLP (float or 1-diemnsional array).
              Size depends on number of nodes in this layer.