def METHOD_NAME(trial, data, objConfig, objParams):
    # set fingerprint parameters from the config file
    #--------------------------------------
    fp_conf = objConfig['config']['model']['fingerprint_specific']
    fp_params = {}
    for key, value in objConfig['config']['model']['fingerprint_specific'].items():
        fp_params.update({key: set_config_param(trial=trial,param_name=key,param=value, all_params=fp_params)})
    logging.info('fingerprting params=%s', fp_params)
    x_column = data['x_column'][0]
    y_column = data['y_column'][0]
    data_processed = {
        'train': None,
        'val': None,
        'test': None,
        'scaler': None,
        'transformer': data['transformer']
    }
    x, y, scaler_svr_physical_data= preprocess_data_phys_and_struct(
            data['train'], fp_params, train_size=1, column_smiles=x_column,
            columns_phys=None, column_y=y_column)
    data_processed['train'] = (x, y)
    data_processed['scaler'] = scaler_svr_physical_data
    objParams['model_params'] = {'fp_params': fp_params, 'scaler':scaler_svr_physical_data}
    if data['val'] is not None:
        x, y, _= preprocess_data_phys_and_struct(
            data['val'], fp_params, train_size=1, column_smiles=x_column,
            columns_phys=None, column_y=y_column, scaler_svr_physical_data=scaler_svr_physical_data)
        data_processed['val'] = (x,y)
    x, y, _= preprocess_data_phys_and_struct(
        data['test'], fp_params, train_size=1, column_smiles=x_column,
        columns_phys=None, column_y=y_column, scaler_svr_physical_data=scaler_svr_physical_data)
    data_processed['test'] = (x,y)
    data_processed = {**data, **data_processed}
    return data_processed