def forward(
        self,
        x,
        *,
        mask = None
    ):

        """
        b - batch
        n - sequence length (within groups)
        g - group dimension
        d - feature dimension (keys)
        e - feature dimension (values)
        i - sequence dimension (source)
        j - sequence dimension (target)
        """

        # prenorm
        normed_x = x 

        # do token shift - a great, costless trick from an independent AI researcher in Shenzhen
        residual = x

        if self.shift_tokens:
            x_shift, x_pass = normed_x.chunk(2, dim = -1)
            x_shift = F.pad(x_shift, (0, 0, 1, -1), value = 0.)
            normed_x = torch.cat((x_shift, x_pass), dim = -1)

        # initial projections
        v, u = self.to_hidden(normed_x).chunk(2, dim = -1)
        qk = self.to_qk(normed_x)

        # offset and scale
        quad_q, lin_q, quad_k, lin_k = self.qk_offset_scale(qk)
        att_v, att_u = self.cal_attention(x, quad_q, lin_q, quad_k, lin_k, v, u)

        out = (att_u*v ) * self.gateActivate(att_v*u)       
        x = x + self.to_out(out)
        return x

        b - batch
        n - sequence length (within groups)
        g - group dimension
        d - feature dimension (keys)
        e - feature dimension (values)
        i - sequence dimension (source)
        j - sequence dimension (target)
        
train/speech_enhancement/models/mossformer2/mossformer2_block.py