How to use the clevercsv.read.reader function in clevercsv

Raises
    ------
    NoDetectionResult
        When the dialect detection fails.

    """
    if encoding is None:
        encoding = get_encoding(filename)
    with open(filename, "r", newline="", encoding=encoding) as fid:
        if dialect is None:
            data = fid.read(num_chars) if num_chars else fid.read()
            dialect = Detector().detect(data, verbose=verbose)
            if dialect is None:
                raise NoDetectionResult()
            fid.seek(0)
        r = reader(fid, dialect)
        yield from r

out = (
            io.StringIO(newline=None)
            if output is None
            else open(output, "w", encoding=encoding)
        )
        if self.option("transpose"):
            with open(path, "r", newline="", encoding=encoding) as fp:
                read = reader(fp, dialect=dialect)
                rows = list(read)
            rows = list(map(list, zip(*rows)))
            write = writer(out, dialect="excel")
            for row in rows:
                write.writerow(row)
        else:
            with open(path, "r", newline="", encoding=encoding) as fp:
                read = reader(fp, dialect=dialect)
                write = writer(out, dialect="excel")
                for row in read:
                    write.writerow(row)
        if output is None:
            self.overwrite(out.getvalue())
        out.close()

def __init__(
        self,
        f,
        fieldnames=None,
        restkey=None,
        restval=None,
        dialect="excel",
        *args,
        **kwds
    ):
        self._fieldnames = fieldnames
        self.restkey = restkey
        self.restval = restval
        self.reader = reader(f, dialect, *args, **kwds)
        self.dialect = dialect
        self.line_num = 0

path, num_chars=num_chars, encoding=encoding, verbose=verbose
            )
        except UnicodeDecodeError:
            return self.line(WARNINGS["unicodedecodeerror"])

        if dialect is None:
            return self.line("Dialect detection failed.")

        out = (
            io.StringIO(newline=None)
            if output is None
            else open(output, "w", encoding=encoding)
        )
        if self.option("transpose"):
            with open(path, "r", newline="", encoding=encoding) as fp:
                read = reader(fp, dialect=dialect)
                rows = list(read)
            rows = list(map(list, zip(*rows)))
            write = writer(out, dialect="excel")
            for row in rows:
                write.writerow(row)
        else:
            with open(path, "r", newline="", encoding=encoding) as fp:
                read = reader(fp, dialect=dialect)
                write = writer(out, dialect="excel")
                for row in read:
                    write.writerow(row)
        if output is None:
            self.overwrite(out.getvalue())
        out.close()

This function is copied from CPython! The only change we've made is to 
        use our dialect detection method.

        """

        # Creates a dictionary of types of data in each column. If any
        # column is of a single type (say, integers), *except* for the first
        # row, then the first row is presumed to be labels. If the type
        # can't be determined, it is assumed to be a string in which case
        # the length of the string is the determining factor: if all of the
        # rows except for the first are the same length, it's a header.
        # Finally, a 'vote' is taken at the end for each column, adding or
        # subtracting from the likelihood of the first row being a header.

        rdr = reader(StringIO(sample), self.sniff(sample))

        header = next(rdr)  # assume first row is header

        columns = len(header)
        columnTypes = {}
        for i in range(columns):
            columnTypes[i] = None

        checked = 0
        for row in rdr:
            # arbitrary number of rows to check, to keep it sane
            if checked > 20:
                break
            checked += 1

            if len(row) != columns: