changed python version and added List

## 0. Requirements

- Python >= 3.7

- Python = 3.9

- numpy and scipy for `generate_test_items.py`, `testBitexact.py` and `self_test.py`

- pytest, pytest-cov and pytest-xdist

(also see requirements.txt)

--- 

## 1.  Scripts and classes for testing IVAS code

    filename: str,

    x: np.ndarray,

    fs: Optional[int] = 48000,

    supress_warning: Optional[bool] = False,

) -> None:

    """

    Write audio file (.pcm, .wav or .raw)

        Numpy 2D array of dimension: number of channels x number of samples

    fs: Optional[int]

        Output sampling rate, required for .pcm or .raw input file, otherwise default = 48000 (Hz)

    supress_warning: Optional[bool]

        If True supress warning when samples clip

    Returns

    -------

    _, file_extension = os.path.splitext(os.path.basename(filename))

    if not supress_warning:

        clipped_samples = np.sum(

            np.logical_or(x < np.iinfo(np.int16).min, x > np.iinfo(np.int16).max)

        )

import logging

import os

import warnings

from typing import Optional, Tuple

from typing import List, Optional, Tuple

import numpy as np

import scipy.interpolate as interp

    pos: np.ndarray,

    interp_1d: Optional[bool] = False,

    weights: Optional[np.ndarray] = None,

    ghost: Optional[list[bool]] = None,

    ghost: Optional[List[bool]] = None,

    SourcePosition: Optional[np.ndarray] = None,

    IR: Optional[np.ndarray] = None,

    phase: Optional[bool] = False,

def add_ghost_speaker_bary(

    SourcePosition: np.ndarray,

    IR: np.ndarray,

) -> Tuple[list[bool], np.ndarray, np.ndarray]:

) -> Tuple[List[bool], np.ndarray, np.ndarray]:

    """

    Adds a ghost speaker at the poles if necessary and indicates result by bool values

    x: np.ndarray,

    IR: np.ndarray,

    nchannels: int,

    lfe_index: Optional[list[int]] = None,

    lfe_index: Optional[List[int]] = None,

) -> np.ndarray:

    """

    Binauralization using fft convolution

        HRIRs array

    nchannels: int

        Maximum number of channels to process

    lfe_index: list[int]

    lfe_index: List[int]

        List of LFE channel indices

    Returns

import shutil

import subprocess as sp

from tempfile import TemporaryDirectory

from typing import List

import numpy as np

def render_masa(

    in_sig: np.ndarray,

    in_meta: str,

    in_meta: List[str],

    out_spfmt: Format,

) -> np.ndarray:

    """

    ----------

    in_sig: np.ndarray

        Input signal with MASA transport channels

    in_meta: list[str]

    in_meta: List[str]

        Input MASA metadata file

    out_spfmt: Format

        Output spatial audio format

import warnings

import platform

import shutil

from typing import Optional, Tuple

from typing import List, Optional, Tuple

import subprocess as sp

from tempfile import TemporaryDirectory

    in_spfmt: spatialaudioformat.Format,

    in_fs: int,

    in_nchans: int,

    in_meta_files: list[str],

    in_meta_files: List[str],

    out_spfmt: spatialaudioformat.Format,

    out_format: str,

    out_fs: int,

        Input sampling frequency

    in_nchans: int

        input number of channels

    in_meta_files: list[str]

    in_meta_files: List[str]

        Metadata files

    out_spfmt: spatialaudioformat.Format

        # write temporary file

        if in_spfmt.isheadphones:

            writefile(tmp_file, in_sig[:, :2], 48000)

            writefile(tmp_file, in_sig[:, :2], 48000, supress_warning=True)

        elif in_spfmt.isloudspeaker:

            writefile(tmp_file, in_sig, 48000)

            writefile(tmp_file, in_sig, 48000, supress_warning=True)

        # run command

        try: