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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def topographic_error(self, X, batch_size=1):
 """ Calculate the topographic error. The topographic error is a measure of the spatial organization of the map. Maps in which the most similar neurons are also close on the grid have low topographic error and indicate that a problem has been learned correctly. Formally, the topographic error is the proportion of units for which the two most similar neurons are not direct neighbors on the map. Parameters X : numpy array. The input data. batch_size : int The batch size to use when calculating the topographic error. Returns ------- error : numpy array A vector of numbers, representing the topographic error for each data point. """

        dist = self.transform(X, batch_size)
        # Sort the distances and get the indices of the two smallest distances
        # for each datapoint.
        res = dist.argsort(1)[:, :2]
        # Lookup the euclidean distance between these points in the distance
        # grid
        dgrid = self.distance_grid.reshape(self.num_neurons, self.num_neurons)
        res = np.asarray([dgrid[x, y] for x, y in res])
        # Subtract 1.0 because 1.0 is the smallest distance.
        return np.sum(res > 1.0) / len(res)