def __init__(self, win_len, win_inc, fft_len=None, win_type='hamming', feature_type='real', fix=True):
        """
        Initializes the ConviSTFT layer.

        Args:
            win_len (int): Length of the window.
            win_inc (int): Window increment (hop length).
            fft_len (int, optional): Length of the FFT. If None, it's computed based on win_len.
            win_type (str, optional): Type of window to use (default is 'hamming').
            feature_type (str, optional): Specifies the output feature type ('real' or 'complex'). Default is 'real'.
            fix (bool, optional): If True, the kernel weights are fixed and not learnable. Default is True.
        """
        super(ConviSTFT, self).__init__()

        if fft_len is None:
            self.fft_len = np.int(2**np.ceil(np.log2(win_len)))  # Calculate fft_len based on win_len
        else:
            self.fft_len = fft_len

        kernel, window = init_kernels(win_len, win_inc, self.fft_len, win_type, invers=True)  # Initialize iSTFT kernel
        self.weight = nn.Parameter(kernel, requires_grad=(not fix))  # Create a learnable parameter for the kernel
        self.feature_type = feature_type
        self.win_type = win_type
        self.win_len = win_len
        self.win_inc = win_inc
        self.stride = win_inc
        self.dim = self.fft_len
        self.register_buffer('window', window)  # Register the window as a buffer
        self.register_buffer('enframe', torch.eye(win_len)[:, None, :])  # Framing matrix for overlap-add method

        Initializes the ConviSTFT layer.

        Args:
            win_len (int): Length of the window.
            win_inc (int): Window increment (hop length).
            fft_len (int, optional): Length of the FFT. If None, it's computed based on win_len.
            win_type (str, optional): Type of window to use (default is 'hamming').
            feature_type (str, optional): Specifies the output feature type ('real' or 'complex'). Default is 'real'.
            fix (bool, optional): If True, the kernel weights are fixed and not learnable. Default is True.
        
clearvoice/clearvoice/models/frcrn_se/conv_stft.py