def build()

in realbook/layers/signal.py [0:0]

• 36 lines of code
• 6 McCabe index (conditional complexity)

    def build(self, input_shape: tf.TensorShape) -> None:
        if input_shape.rank > 3 or input_shape.rank <= 1:
            raise ValueError(
                "realbook.layers.signal.Istft received an input shape of "
                f"{input_shape}, but only supports inputs shaped "
                "like (num_frames, num_bins) or (num_batches, num_frames, num_bins)."
            )

        self.window = _create_padded_window(self.window_fn, self.window_length, self.fft_length)(
            self.fft_length
        )  # type: ignore

        self.window_sum = librosa_filters.window_sumsquare(  # type: ignore
            window=self.window.numpy(),
            n_frames=input_shape[0] if input_shape.rank == 2 else input_shape[1],
            win_length=self.window_length,
            n_fft=self.fft_length,
            hop_length=self.hop_length,
            dtype=self.dtypes_type.as_numpy_dtype,
        )

        self.window_sum = tf.constant(
            np.where(
                self.window_sum > np.finfo(self.dtypes_type.as_numpy_dtype).tiny,
                self.window_sum,
                np.ones_like(self.window_sum),
            ),
            self.dtypes_type.real_dtype,
        )

        self.slice_op = tf.keras.layers.Lambda(lambda x: x)
        if self.center:
            if input_shape.rank == 2:  # unbatched
                self.slice_op = tf.keras.layers.Lambda(
                    lambda x: x[int(self.fft_length // 2) : -int(self.fft_length // 2)]
                )
            else:  # batched
                self.slice_op = tf.keras.layers.Lambda(
                    lambda x: x[:, int(self.fft_length // 2) : -int(self.fft_length // 2)]
                )