//  [python]
def data_by_type(self, idx, return_type, e_start, e_end, start, n_atoms):
        if return_type == 'dgl_graph':
            g = self.get_graph(idx, e_start, e_end, n_atoms).to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device)
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            g.edata['feat'] = self.e_features_tensor[e_start: e_end].to(self.device)
            return g
        elif return_type == 'dgl_graph3d':
            g = self.get_graph(idx, e_start, e_end, n_atoms).to(self.device)
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device)
            return g
        elif return_type == 'complete_graph':  # complete graph without self loops
            g = self.get_complete_graph(idx, n_atoms).to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device)
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            g.edata['d'] = torch.norm(g.ndata['x'][g.edges()[0]] - g.ndata['x'][g.edges()[1]], p=2, dim=-1).unsqueeze(
                -1)

            # set edge features with padding for virtual edges
            bond_features = self.e_features_tensor[e_start: e_end].to(self.device)
            e_features = self.bond_padding_indices.expand(n_atoms * n_atoms, -1)
            edge_indices = self.edge_indices[:, e_start: e_end].to(self.device)
            bond_indices = edge_indices[0] * n_atoms + edge_indices[1]
            # overwrite the bond features
            e_features = e_features.scatter(dim=0, index=bond_indices[:, None].expand(-1, bond_features.shape[1]),
                                            src=bond_features)
            src, dst = self.get_pairwise(n_atoms)
            g.edata['feat'] = e_features[src * n_atoms + dst]
            if self.dist_embedding:
                g.edata['d_rbf'] = self.dist_embedder(g.edata['feat']).to(self.device)
            return g
        elif return_type == 'complete_graph3d':
            g = self.get_complete_graph(idx, n_atoms).to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device)
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            g.edata['d'] = torch.norm(g.ndata['x'][g.edges()[0]] - g.ndata['x'][g.edges()[1]], p=2, dim=-1).unsqueeze(
                -1)
            if self.dist_embedding:
                g.edata['d_rbf'] = self.dist_embedder(g.edata['feat']).to(self.device)
            return g
        if return_type == 'mol_complete_graph':
            g = self.get_mol_complete_graph(idx, e_start, e_end, n_atoms).to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device)
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            if self.e_features_tensor != None:
                g.edges['bond'].data['feat'] = self.e_features_tensor[e_start: e_end].to(self.device)
            return g
        if return_type == 'san_graph':
            g = self.get_complete_graph(idx, n_atoms).to(self.device)
            g.ndata['feat'] = self.features_tensor[start: start + n_atoms].to(self.device).float()
            g.ndata['x'] = self.coordinates[start: start + n_atoms].to(self.device)
            eig_vals = self.eig_vals[idx].to(self.device)
            sign_flip = torch.rand(eig_vals.shape[0], device=self.device)
            sign_flip[sign_flip >= 0.5] = 1.0
            sign_flip[sign_flip < 0.5] = -1.0
            eig_vecs = self.eig_vecs[start: start + n_atoms].to(self.device) * sign_flip.unsqueeze(0)
            eig_vals = eig_vals.unsqueeze(0).repeat(n_atoms, 1)
            g.ndata['pos_enc'] = torch.stack([eig_vals, eig_vecs], dim=-1)
            if self.e_features_tensor != None:
                e_features = self.e_features_tensor[e_start: e_end].to(self.device).float()
                g.edata['feat'] = torch.zeros(g.number_of_edges(), e_features.shape[1], dtype=torch.float32,
                                              device=self.device)
                g.edata['real'] = torch.zeros(g.number_of_edges(), dtype=torch.long, device=self.device)
                edge_indices = self.edge_indices[:, e_start: e_end].to(self.device)
                g.edges[edge_indices[0], edge_indices[1]].data['feat'] = e_features
                g.edges[edge_indices[0], edge_indices[1]].data['real'] = torch.ones(e_features.shape[0],
                                                                                    dtype=torch.long,
                                                                                    device=self.device)  # This indicates real edges
            return g
        elif return_type == 'se3Transformer_graph' or return_type == 'se3Transformer_graph3d':
            g = self.get_graph(idx, e_start, e_end, n_atoms).to(self.device)
            x = self.coordinates[start: start + n_atoms].to(self.device)
            if self.transform:
                x = self.transform(x)
            g.ndata['x'] = x
            edge_indices = self.edge_indices[:, e_start: e_end].to(self.device)
            g.edata['d'] = x[edge_indices[0]] - x[edge_indices[1]]
            if self.e_features_tensor != None and return_type == 'se3Transformer_graph':
                g.edata['feat'] = self.e_features_tensor[e_start: e_end].to(self.device)
            return g

        elif return_type == 'padded_e_features':
            bond_features = self.e_features_tensor[e_start: e_end].to(self.device)
            e_features = self.bond_padding_indices.expand(n_atoms * n_atoms, -1)
            edge_indices = self.edge_indices[:, e_start: e_end].to(self.device)
            bond_indices = edge_indices[0] * n_atoms + edge_indices[1]
            # overwrite the bond features
            return e_features.scatter(dim=0, index=bond_indices[:, None].expand(-1, bond_features.shape[1]),
                                      src=bond_features)
        elif return_type == 'pytorch_geometric_graph':
            edge_features = self.e_features_tensor[e_start: e_end].to(self.device)
            edge_indices = self.edge_indices[:, e_start: e_end].to(self.device)
            R_i = self.coordinates[start: start + n_atoms].to(self.device)
            z_i = self.features_tensor[start: start + n_atoms].to(self.device)
            return torch_geometric.data.Data(pos=R_i, z=z_i, edge_attr=edge_features, edge_index=edge_indices)
        elif return_type == 'pairwise_indices':
            src, dst = self.get_pairwise(n_atoms)
            return torch.stack([src, dst], dim=0).to(self.device)
        elif return_type == 'raw_features':
            return self.features_tensor[start: start + n_atoms].to(self.device)
        elif return_type == 'constant_ones':
            return torch.ones_like(self.features_tensor[start: start + n_atoms], device=self.device)
        elif return_type == 'n_atoms':
            return self.meta_dict['n_atoms'][n_atoms]
        elif return_type == 'coordinates':
            return self.coordinates[start: start + n_atoms].to(self.device)
        elif return_type == 'positional_encoding':
            eig_vals = self.eig_vals[idx].to(self.device)
            sign_flip = torch.rand(eig_vals.shape[0], device=self.device)
            sign_flip[sign_flip >= 0.5] = 1.0
            sign_flip[sign_flip < 0.5] = -1.0
            eig_vecs = self.eig_vecs[start: start + n_atoms].to(self.device) * sign_flip.unsqueeze(0)
            eig_vals = eig_vals.unsqueeze(0).repeat(n_atoms, 1)
            return torch.stack([eig_vals, eig_vecs], dim=-1)
        elif return_type == 'mol_id':
            return self.meta_dict['mol_id'][idx]
        elif return_type == 'targets':
            return self.targets[idx]
        elif return_type == 'edge_indices':
            return self.meta_dict['edge_indices'][:, e_start: e_end]
        elif return_type == 'smiles':
            return self.smiles[self.meta_dict['mol_id'][idx]]
        else:
            raise Exception(f'return type not supported: ', return_type)