code: def run(self, x):
          
          # measure the data and check if the dimmension agree
          try:    
              x = np.array(x)
          except:
              raise ValueError('Impossible to convert x to a numpy array')
          N = len(x)   
          # create empty arrays     
          if self.outputs == 1:
              y = np.zeros(N)
          else:
              y = np.zeros((N, self.outputs))
          # predict data in loop        
          for k in range(N):
              y[k] = self.predict(x[k])
          return y
docs: Function for batch usage of already trained and tested MLP.
  
          **Args:**
  
          * `x` : input array (2-dimensional array).
              Every row represents one input vector (features).
  
          **Returns:**
          
          * `y`: output vector (n-dimensional array). Every row represents
              output (outputs) for an input vector.