How to use the mlxtend.frequent_patterns.fpcommon.valid_input_check function in mlxtend

Examples
        -----------
        For usage examples, please see
        http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/

        """
        out = (np.sum(_x, axis=0) / _n_rows)
        return np.array(out).reshape(-1)

    if min_support <= 0.:
        raise ValueError('`min_support` must be a positive '
                         'number within the interval `(0, 1]`. '
                         'Got %s.' % min_support)

    fpc.valid_input_check(df)

    # sparse attribute exists for both deprecated SparseDataFrame and
    # DataFrame with SparseArray (pandas >= 0.24); to_coo attribute
    # exists only for the former, thus it is checked first to distinguish
    # between SparseDataFrame and DataFrame with SparseArray.
    if hasattr(df, "to_coo"):
        # SparseDataFrame with pandas < 0.24
        if df.size == 0:
            X = df.values
        else:
            X = df.to_coo().tocsc()
        is_sparse = True
    elif hasattr(df, "sparse"):
        # DataFrame with SparseArray (pandas >= 0.24)
        if df.size == 0:
            X = df.values

verbose : int (default: 0)
      Shows the stages of conditional tree generation.

    Returns
    -----------
    pandas DataFrame with columns ['support', 'itemsets'] of all maximal
      itemsets that are >= `min_support` and < than `max_len`
      (if `max_len` is not None).
      Each itemset in the 'itemsets' column is of type `frozenset`,
      which is a Python built-in type that behaves similarly to
      sets except that it is immutable
      (For more info, see
      https://docs.python.org/3.6/library/stdtypes.html#frozenset).

    """
    fpc.valid_input_check(df)

    if min_support <= 0.:
        raise ValueError('`min_support` must be a positive '
                         'number within the interval `(0, 1]`. '
                         'Got %s.' % min_support)

    colname_map = None
    if use_colnames:
        colname_map = {idx: item for idx, item in enumerate(df.columns)}

    tree, rank = fpc.setup_fptree(df, min_support)

    minsup = math.ceil(min_support * len(df.values))  # min support as count
    generator = fpmax_step(tree, minsup, MFITree(rank),
                           colname_map, max_len, verbose)

verbose : int (default: 0)
      Shows the stages of conditional tree generation.

    Returns
    -----------
    pandas DataFrame with columns ['support', 'itemsets'] of all itemsets
      that are >= `min_support` and < than `max_len`
      (if `max_len` is not None).
      Each itemset in the 'itemsets' column is of type `frozenset`,
      which is a Python built-in type that behaves similarly to
      sets except that it is immutable
      (For more info, see
      https://docs.python.org/3.6/library/stdtypes.html#frozenset).

    """
    fpc.valid_input_check(df)

    if min_support <= 0.:
        raise ValueError('`min_support` must be a positive '
                         'number within the interval `(0, 1]`. '
                         'Got %s.' % min_support)

    colname_map = None
    if use_colnames:
        colname_map = {idx: item for idx, item in enumerate(df.columns)}

    tree, _ = fpc.setup_fptree(df, min_support)
    minsup = math.ceil(min_support * len(df.index))  # min support as count
    generator = fpg_step(tree, minsup, colname_map, max_len, verbose)

    return fpc.generate_itemsets(generator, len(df.index), colname_map)