code: def backward(self, diff_x, influences, activations, **kwargs):
          
          bmu = self._get_bmu(activations)
          influence = influences[bmu]
          update = np.multiply(diff_x, influence)
          return update
docs: Backward pass through the network, including update.
  
          Parameters
          ----------
          diff_x : numpy array
              A matrix containing the differences between the input and neurons.
          influences : numpy array
              A matrix containing the influence each neuron has on each
              other neuron. This is used to calculate the updates.
          activations : numpy array
              The activations each neuron has to each data point. This is used
              to calculate the BMU.
  
          Returns
          -------
          update : numpy array
              A numpy array containing the updates to the neurons.