def __init__(self, d_model, feedback=True):
        """
        feedback: control whether cell state feeds back into itself. If False, this is essentially a QRNN reduce
        """
        super().__init__()
        self.d_model = d_model
        self.feedback = feedback
        if self.feedback:
            self.W_fc = nn.Parameter(torch.randn(self.d_model))

        feedback: control whether cell state feeds back into itself. If False, this is essentially a QRNN reduce
        
analyze/convnexts4nd/src/models/sequence/rnns/sru.py