def METHOD_NAME():
    SEQ_LEN = 100
    num_spk = 2
    BS = 2
    separator = TCNSeparator(
        input_dim=128,
        num_spk=2,
        layer=2,
        stack=3,
        bottleneck_dim=32,
        hidden_dim=64,
        kernel=3,
        causal=True,
        norm_type="cLN",
    )
    separator.eval()
    input_feature = torch.randn((BS, SEQ_LEN, 128))
    ilens = torch.LongTensor([SEQ_LEN] * BS)
    with torch.no_grad():
        seq_output, _, _ = separator.forward(input_feature, ilens=ilens)
        state = None
        stream_outputs = []
        for i in range(SEQ_LEN):
            frame = input_feature[:, i : i + 1, :]
            frame_out, state, _ = separator.forward_streaming(frame, state)
            stream_outputs.append(frame_out)
        for i in range(SEQ_LEN):
            for s in range(num_spk):
                torch.testing.assert_close(
                    stream_outputs[i][s], seq_output[s][:, i : i + 1, :]
                )