def METHOD_NAME(data_path, train_save_name, val_save_name, max_pairs, train_split, headers):
    """
    Generates and saves UMLS self alignment pretraining train and validation data. Takes the raw .RRF UMLS 
    data file and creates different pair combinations for entities with the same CUI. Each row in the output
    will be formatted as 'CUI EntitySynonym1 EntitySynonym2' with each item in a row separated by tabs.
    Saves two .tsv output files, one for the train split and one for the validation split.
    Only data marked as English is added to the train and val splits. 
    Arguments:
        data_path (str): path to MRCONSO.RRF UMLS data file
        train_save_name (str): path to where training data will be saved
        val_save_name (str): path to where validation data will be saved
        max_pairs (int): max number of pairs for any one CUI added to the train 
                   or validation splits
        train_split (float): precentage of raw data to be added to train set split
        headers (list): column lables within MRCONSO.RRF
    """
    print("Loading training data file...")
    df = pd.read_table(data_path, names=headers, index_col=False, delimiter='|')
    train_file = open(train_save_name, 'w')
    val_file = open(val_save_name, 'w')
    cui = df["CUI"].iloc[0]
    names = []
    random.seed(2021)
    for idx in tqdm(range(len(df))):
        # Address incorrectly formatted data
        if type(df["STR"].iloc[idx]) != str or "|" in df["STR"].iloc[idx]:
            continue
        # Collect all english concept strings matching the current CUI
        if df["CUI"].iloc[idx] == cui and df["LAT"].iloc[idx] == "ENG":
            concept_string = df["STR"].iloc[idx]
            names.append(concept_string)
        else:
            # Pair off concept synonyms to make training and val sets
            pairs = list(itertools.combinations(names, 2))
            if len(pairs) == 0:
                # Not enough concepts gathered to make a pair
                cui = df["CUI"].iloc[idx]
                names = [df["STR"].iloc[idx]]
                continue
            # Removing leading C to convert label string to int
            cui = int(cui[1:])
            random.shuffle(pairs)
            # Keep up to max pairs number pairs for any one concept
            for pair in pairs[:max_pairs]:
                # Want concepts in train and val splits to be randomly selected and mutually exclusive
                add_to_train = random.random()
                if add_to_train <= train_split:
                    train_file.write(f'{cui}\t{pair[0]}\t{pair[1]}\n')
                else:
                    val_file.write(f'{cui}\t{pair[0]}\t{pair[1]}\n')
            # Switch to next concept
            cui = df["CUI"].iloc[idx]
            names = [df["STR"].iloc[idx]]
    train_file.close()
    val_file.close()
    print("Finished making training and validation data")