How to use the autogluon.utils.tabular.utils.s3_utils.s3_path_to_bucket_prefix function in autogluon

format = 'multipart_s3'
    elif path[-1] == '/' and not s3_utils.is_s3_url(path):  # and path[:2] != 's3'
        format = 'multipart_local'
    elif '.parquet' in path or path[-1] == '/':
        format = 'parquet'
    else:
        format = 'csv'

    if format == 'pointer':
        content_path = load_pointer.get_pointer_content(path)
        return load(path=content_path, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep, dtype=dtype, 
                    error_bad_lines=error_bad_lines, header=header, names=names, format=None, nrows=nrows, skiprows=skiprows, 
                    usecols=usecols, low_memory=low_memory, converters=converters, filters=filters, sample_count=sample_count, 
                    worker_count=worker_count, multiprocessing_method=multiprocessing_method)
    elif format == 'multipart_s3':
        bucket, prefix = s3_utils.s3_path_to_bucket_prefix(path)
        return load_multipart_s3(bucket=bucket, prefix=prefix, columns_to_keep=columns_to_keep, dtype=dtype, filters=filters, 
                                 sample_count=sample_count, worker_count=worker_count, multiprocessing_method=multiprocessing_method)  # TODO: Add arguments!
    elif format == 'multipart_local':
        paths = [join(path, f) for f in listdir(path) if (isfile(join(path, f))) & (f.startswith('part-'))]
        return load_multipart(
            paths=paths, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep,
            dtype=dtype, error_bad_lines=error_bad_lines, header=header, names=names, format=None,
            nrows=nrows, skiprows=skiprows, usecols=usecols, low_memory=low_memory, converters=converters,
            filters=filters,
            worker_count=worker_count,
            multiprocessing_method=multiprocessing_method,
        )
    elif format == 'parquet':
        try:
            df = pd.read_parquet(path, columns=columns_to_keep, engine='fastparquet')  # TODO: Deal with extremely strange issue resulting from torch being present in package, will cause read_parquet to either freeze or Segmentation Fault when performing multiprocessing
            column_count_full = len(df.columns)

type = 'csv'
    if 's3' not in path[:2]:
        is_local = True
    else:
        is_local = False
    column_count = len(list(df.columns.values))
    row_count = df.shape[0]
    if is_local:
        os.makedirs(os.path.dirname(path), exist_ok=True)
    if type == 'csv':
        if is_local:
            df.to_csv(path, index=index, sep=sep, header=header)
        else:
            buffer = StringIO()
            df.to_csv(buffer, index=index, sep=sep, header=header)
            bucket, prefix = s3_utils.s3_path_to_bucket_prefix(s3_path=path)
            s3_resource = boto3.resource('s3')
            s3_resource.Object(bucket, prefix).put(Body=buffer.getvalue(), ACL='bucket-owner-full-control')
        if verbose:
            logger.log(15, "Saved " +str(path)+" | Columns = "+str(column_count)+" | Rows = "+str(row_count))
    elif type == 'parquet':
        try:
            df.to_parquet(path, compression=compression, engine='fastparquet')  # TODO: Might be slower than pyarrow in multiprocessing
        except:
            df.to_parquet(path, compression=compression, engine='pyarrow')
        if verbose:
            logger.log(15, "Saved "+str(path)+" | Columns = "+str(column_count)+" | Rows = "+str(row_count))
    elif type == 'multipart_s3':
        bucket, prefix = s3_utils.s3_path_to_bucket_prefix(s3_path=path)
        s3_utils.delete_s3_prefix(bucket=bucket, prefix=prefix)  # TODO: Might only delete the first 1000!
        save_multipart(path=path, df=df, index=index, verbose=verbose, type='parquet', sep=sep, compression=compression, header=header, json_dump_columns=None)
    elif type == 'multipart_local':

def get_pointer_content(path, verbose=True):
    if s3_utils.is_s3_url(path):
        bucket, key = s3_utils.s3_path_to_bucket_prefix(path)
        s3 = boto3.resource('s3')
        obj = s3.Object(bucket, key)
        content_path = obj.get()['Body'].read().decode('utf-8')
    else:
        os.makedirs(os.path.dirname(path), exist_ok=True)
        f = open(path, "r")
        content_path = f.read()
        f.close()
    if verbose:
        logger.log(15, 'Loaded pointer file '+str(path)+' pointing to '+str(content_path))

    return content_path