def METHOD_NAME():
    """Assert the output shapes of the model in different modes"""
    # VANILLA MODEL
    model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False))
    x = T.randint(0, 10, (2, 21))
    x_lengths = T.randint(10, 22, (2,))
    x_lengths[-1] = 21
    x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
    durations = T.randint(1, 4, (2, 21))
    durations = durations * x_mask.squeeze(1)
    y_lengths = durations.sum(1)
    y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
    outputs = model.forward(x, x_lengths, y_lengths, dr=durations)
    assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
    assert outputs["durations_log"].shape == (2, 21)
    assert outputs["durations"].shape == (2, 21)
    assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
    assert (outputs["x_mask"] - x_mask).sum() == 0.0
    assert (outputs["y_mask"] - y_mask).sum() == 0.0
    assert outputs["alignment_soft"] is None
    assert outputs["alignment_mas"] is None
    assert outputs["alignment_logprob"] is None
    assert outputs["o_alignment_dur"] is None
    assert outputs["pitch_avg"] is None
    assert outputs["pitch_avg_gt"] is None
    # USE PITCH
    model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False))
    x = T.randint(0, 10, (2, 21))
    x_lengths = T.randint(10, 22, (2,))
    x_lengths[-1] = 21
    x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
    durations = T.randint(1, 4, (2, 21))
    durations = durations * x_mask.squeeze(1)
    y_lengths = durations.sum(1)
    y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
    pitch = T.rand(2, 1, y_lengths.max())
    outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch)
    assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
    assert outputs["durations_log"].shape == (2, 21)
    assert outputs["durations"].shape == (2, 21)
    assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
    assert (outputs["x_mask"] - x_mask).sum() == 0.0
    assert (outputs["y_mask"] - y_mask).sum() == 0.0
    assert outputs["pitch_avg"].shape == (2, 1, 21)
    assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
    assert outputs["alignment_soft"] is None
    assert outputs["alignment_mas"] is None
    assert outputs["alignment_logprob"] is None
    assert outputs["o_alignment_dur"] is None
    # USE ALIGNER NETWORK
    model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True))
    x = T.randint(0, 10, (2, 21))
    x_lengths = T.randint(10, 22, (2,))
    x_lengths[-1] = 21
    x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
    durations = T.randint(1, 4, (2, 21))
    durations = durations * x_mask.squeeze(1)
    y_lengths = durations.sum(1)
    y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
    y = T.rand(2, y_lengths.max(), 80)
    outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y)
    assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
    assert outputs["durations_log"].shape == (2, 21)
    assert outputs["durations"].shape == (2, 21)
    assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
    assert (outputs["x_mask"] - x_mask).sum() == 0.0
    assert (outputs["y_mask"] - y_mask).sum() == 0.0
    assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
    assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
    assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
    assert outputs["o_alignment_dur"].shape == (2, 21)
    assert outputs["pitch_avg"] is None
    assert outputs["pitch_avg_gt"] is None
    # USE ALIGNER NETWORK AND PITCH
    model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True))
    x = T.randint(0, 10, (2, 21))
    x_lengths = T.randint(10, 22, (2,))
    x_lengths[-1] = 21
    x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
    durations = T.randint(1, 4, (2, 21))
    durations = durations * x_mask.squeeze(1)
    y_lengths = durations.sum(1)
    y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
    y = T.rand(2, y_lengths.max(), 80)
    pitch = T.rand(2, 1, y_lengths.max())
    outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y)
    assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
    assert outputs["durations_log"].shape == (2, 21)
    assert outputs["durations"].shape == (2, 21)
    assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
    assert (outputs["x_mask"] - x_mask).sum() == 0.0
    assert (outputs["y_mask"] - y_mask).sum() == 0.0
    assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
    assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
    assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
    assert outputs["o_alignment_dur"].shape == (2, 21)
    assert outputs["pitch_avg"].shape == (2, 1, 21)
    assert outputs["pitch_avg_gt"].shape == (2, 1, 21)