How to use the asteroid.utils.tensors_to_device function in asteroid

nondefault_nsrc=model.separator.n_sources,
                           segment=None, normalize_audio=True)
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    cnt = 0
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix.unsqueeze(0))
        min_len = min(est_sources.shape[-1], sources.shape[-1], mix.shape[-1])
        est_sources = est_sources[..., :min_len]
        mix, sources = mix[..., :min_len], sources[..., :min_len]
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:

model_device = next(model.parameters()).device
    test_set = Wsj0mixDataset(conf['test_dir'], n_src=conf['n_src'],
                              segment=None)
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, mode='pairwise')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        if conf['train_conf']['training']['loss_alpha'] == 1:
            # If Deep clustering only, use DC masks.
            est_sources, dic_out = model.dc_head_separate(mix[None, None])
        else:
            # If Chimera, use mask-inference head masks
            est_sources, dic_out = model.separate(mix[None, None])

        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mix[idx][0]

n_src=conf['train_conf']['data']['n_src'],
                        segment=None)  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    eval_save_dir = os.path.join(conf['exp_dir'], conf['out_dir'])
    ex_save_dir = os.path.join(eval_save_dir, 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix.unsqueeze(0))
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix.cpu().data.numpy()
        sources_np = sources.cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
        # For each utterance, we get a dictionary with the mixture path,
        # the input and output metrics
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mixture_path
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:

model_device = next(model.parameters()).device
    test_set = KinectWsjMixDataset(conf['test_dir'], n_src=conf['n_src'],
                              segment=None)
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources, noises = tensors_to_device(test_set[idx], device=model_device)
        mix = mix[..., 0]
        sources = sources[...,0]
        #noise = noise[..., 0]
        if conf['train_conf']['training']['loss_alpha'] == 1:
            # If Deep clustering only, use DC masks.
            est_sources, dic_out = model.dc_head_separate(mix[None, None])
        else:
            # If Chimera, use mask-inference head masks
            est_sources, dic_out = model.separate(mix[None, None])

        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,

test_set = LibriMix(conf['test_dir'], None,
                        conf['sample_rate'],
                        conf['train_conf']['data']['n_src'])

    loss_func = PITLossWrapper(pairwise_neg_sisdr, mode='pairwise')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples_mss_8K/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)

        est_sources = model(mix.unsqueeze(0))

        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)

        mix_np = mix.cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        # est_sources_np[0, :] = est_sources_np[0, :] / \
        #                        np.max(est_sources_np, axis=1)[0] * \
        #                        np.max(sources_np, axis=1)[0]
        # est_sources_np[1, :] = est_sources_np[1, :] / \
        #                        np.max(est_sources_np, axis=1)[1] * \
        #                        np.max(sources_np, axis=1)[1]
        # For each utterance, we get a dictionary with the mixture path,

sample_rate=conf['sample_rate'],
                           nondefault_nsrc=model.masker.n_src,
                           segment=None)  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix[None, None])
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],

sample_rate=conf['sample_rate'],
                           nondefault_nsrc=model.n_src,
                           segment=None)  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, mode='pairwise')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix[None, None])
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],

sample_rate=conf['sample_rate'],
                           nondefault_nsrc=model.masker.n_src,
                           segment=None)  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix[None, None])
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)