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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 train_local(self, closest_point, label_vector_description=None, N=None, pivot=True, **kwargs):
 """ Train the model in a Cannon-like fashion using the grid points as labels and the intensities as normalsied rest-frame fluxes within some local regime. """


        lv = self._cannon_label_vector if label_vector_description is None else\
            self._interpret_label_vector(label_vector_description)

        # By default we will train to the nearest 10% of the grid.
        # If grid subset is a fraction, scale it to real numbers.
        if N is None:
            N = self._configuration.get("settings", {}).get("grid_subset",
                0.10)
            if 1 >= N > 0:
                N = int(np.round(N * self.grid_points.size))

        logger.debug("Using {} nearest points for local Cannon model".format(N))

        # Use closest N points.
        dtype = [(name, '<f8') for name in self.grid_points.dtype.names]
        grid_points \
            = self.grid_points.astype(dtype).view(float).reshape(-1, len(dtype))

        distance = np.sum(np.abs(grid_points - np.array(closest_point))/
            np.ptp(grid_points, axis=0), axis=1)
        grid_indices = np.argsort(distance)[:N]
        
        lv_array, _, offsets = _build_label_vector_array(
            self.grid_points[grid_indices], lv, pivot=pivot)
        return self._train(lv_array, grid_indices, offsets, lv, **kwargs)