code: def train_global(self, label_vector_description=None, N=None, limits=None,
          pivot=True, **kwargs):
          
  
          lv = self._cannon_label_vector if label_vector_description is None else\
              self._interpret_label_vector(label_vector_description)
          lv_array, grid_indices, offsets = _build_label_vector_array(
              self.grid_points, lv, N, limits, pivot=pivot)
          return self._train(lv_array, grid_indices, offsets, lv, **kwargs)
docs: Train the model in a Cannon-like fashion using the grid points as labels
          and the intensities as normalised rest-frame fluxes.