How to use the torchaudio.load_wav function in torchaudio

'blackman_coeff', 'energy_floor', 'frame_length', 'frame_shift', 'high_freq', 'htk_compat',
        'low_freq', 'num_mel_bins', 'preemphasis_coefficient', 'raw_energy', 'remove_dc_offset',
        'round_to_power_of_two', 'snip_edges', 'subtract_mean', 'use_energy', 'use_log_fbank',
        'use_power', 'vtln_high', 'vtln_low', 'vtln_warp', 'window_type']
    fn_split = fn.split('-')
    assert len(fn_split) == len(arr), ('Len mismatch: %d and %d' % (len(fn_split), len(arr)))
    inputs = {arr[i]: utils.parse(fn_split[i]) for i in range(len(arr))}

    # print flags for C++
    s = ' '.join(['--' + arr[i].replace('_', '-') + '=' + fn_split[i] for i in range(len(arr))])
    logging.info(exe_path + ' --dither=0.0 --debug-mel=true ' + s + ' ' + scp_path + ' ' + out_fn)
    logging.info()
    # print args for python
    inputs['dither'] = 0.0
    logging.info(inputs)
    sound, sample_rate = torchaudio.load_wav(sound_path)
    kaldi_output_dict = {k: v for k, v in torchaudio.kaldi_io.read_mat_ark(out_fn)}
    res = torchaudio.compliance.kaldi.fbank(sound, **inputs)
    torch.set_printoptions(precision=10, sci_mode=False)
    logging.info(res)
    logging.info(kaldi_output_dict['my_id'])

def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_files,
                                expected_num_args, get_output_fn, atol=1e-5, rtol=1e-8):
        """
        Inputs:
            sound_filepath (str): The location of the sound file
            filepath_key (str): A key to `test_filepaths` which matches which files to use
            expected_num_files (int): The expected number of kaldi files to read
            expected_num_args (int): The expected number of arguments used in a kaldi configuration
            get_output_fn (Callable[[Tensor, List], Tensor]): A function that takes in a sound signal
                and a configuration and returns an output
            atol (float): absolute tolerance
            rtol (float): relative tolerance
        """
        sound, sample_rate = torchaudio.load_wav(sound_filepath)
        files = self.test_filepaths[filepath_key]

        assert len(files) == expected_num_files, ('number of kaldi %s file changed to %d' % (filepath_key, len(files)))

        for f in files:
            print(f)

            # Read kaldi's output from file
            kaldi_output_path = os.path.join(self.kaldi_output_dir, f)
            kaldi_output_dict = {k: v for k, v in torchaudio.kaldi_io.read_mat_ark(kaldi_output_path)}

            assert len(kaldi_output_dict) == 1 and 'my_id' in kaldi_output_dict, 'invalid test kaldi ark file'
            kaldi_output = kaldi_output_dict['my_id']

            # Construct the same configuration used by kaldi
            args = f.split('-')

def test_resample_waveform_identity_size(self):
        sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath)
        downsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate)
        self.assertTrue(downsample_sound.size(-1) == sound.size(-1))

def test_resample_waveform_upsample_size(self):
        sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath)
        upsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate * 2)
        self.assertTrue(upsample_sound.size(-1) == sound.size(-1) * 2)

def __getitem__(self, index):
        import torchaudio
        import torchaudio.compliance.kaldi as kaldi
        tgt_item = self.tgt[index] if self.tgt is not None else None

        path = self.aud_paths[index]
        if not os.path.exists(path):
            raise FileNotFoundError("Audio file not found: {}".format(path))
        sound, sample_rate = torchaudio.load_wav(path)
        output = kaldi.fbank(
            sound,
            num_mel_bins=self.num_mel_bins,
            frame_length=self.frame_length,
            frame_shift=self.frame_shift
        )
        output_cmvn = data_utils.apply_mv_norm(output)

        return {"id": index, "data": [output_cmvn.detach(), tgt_item]}

def __getitem__(self, index):
        utt_id, path = self.file_list[index]

        if self.from_kaldi:
            feature = kio.load_mat(path)
        else:
            wavform, sample_frequency = ta.load_wav(path)
            feature = compute_fbank(wavform, num_mel_bins=self.params['num_mel_bins'], sample_frequency=sample_frequency, dither=0.0)

        if self.params['apply_cmvn']:
            spk_id = self.utt2spk[utt_id]
            stats = kio.load_mat(self.cmvns[spk_id])
            feature = apply_cmvn(feature, stats)

        if self.params['normalization']:
            feature = normalization(feature)
            
        if self.apply_spec_augment:
            feature = spec_augment(feature)

        feature_length = feature.shape[0]
        targets = self.targets_dict[utt_id]
        targets_length = len(targets)