code: def _check_column_dtypes_novelty_coverage(func):
      """Checks columns of DataFrame inputs
  
      This includes the checks on:
  
      * whether the input columns exist in the input DataFrames
      * whether the data types of col_user as well as col_item are matched in the two input DataFrames.
      * whether reco_df contains any user_item pairs that are already shown in train_df
  
      Args:
          func (function): function that will be wrapped
  
      Returns:
          function: Wrapper function for checking dtypes.
      """
  
      @wraps(func)
      def check_column_dtypes_novelty_coverage_wrapper(
          train_df,
          reco_df,
          col_user=DEFAULT_USER_COL,
          col_item=DEFAULT_ITEM_COL,
          *args,
          **kwargs,
      ):
          """Check columns of DataFrame inputs
  
          Args:
              train_df (pandas.DataFrame): Data set with historical data for users and items they
                  have interacted with; contains col_user, col_item. Assumed to not contain any duplicate rows.
                  Interaction here follows the *item choice model* from Castells et al.
              reco_df (pandas.DataFrame): Recommender's prediction output, containing col_user, col_item,
                  col_relevance (optional). Assumed to not contain any duplicate user-item pairs.
              col_user (str): User id column name.
              col_item (str): Item id column name.
  
          """
  
          if not has_columns(train_df, [col_user, col_item]):
              raise ValueError("Missing columns in train_df DataFrame")
          if not has_columns(reco_df, [col_user, col_item]):
              raise ValueError("Missing columns in reco_df DataFrame")
          if not has_same_base_dtype(
              train_df, reco_df, columns=[col_user, col_item]
          ):
              raise ValueError(
                  "Columns in provided DataFrames are not the same datatype"
              )
  
          count_intersection = pd.merge(
              train_df, reco_df, how="inner", on=[col_user, col_item]
          ).shape[0]
          if count_intersection != 0:
              raise Exception(
                  "reco_df should not contain any user_item pairs that are already shown in train_df"
              )
  
          return func(
              train_df=train_df,
              reco_df=reco_df,
              col_user=col_user,
              col_item=col_item,
              *args,
              **kwargs,
          )
  
      return check_column_dtypes_novelty_coverage_wrapper
  
imports: import numpy
  import pandas
  from functools import wraps
  from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, roc_auc_score, log_loss
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, DEFAULT_RELEVANCE_COL, DEFAULT_SIMILARITY_COL, DEFAULT_ITEM_FEATURES_COL, DEFAULT_ITEM_SIM_MEASURE, DEFAULT_K, DEFAULT_THRESHOLD
  from recommenders.datasets.pandas_df_utils import has_columns, has_same_base_dtype, lru_cache_df
label_desc: Use image node_id 18 for calling a global function with example usage: _check_column_dtypes_novelty_coverage(func) and returns: check_column_dtypes_novelty_coverage_wrapper, func