def METHOD_NAME(self, x):
    list_x = []
    for b in range(self.B):
        list_x_row = [x[b, t, : self.y_len[b]+1] for t in range(self.f_len[b])]
        x_row = torch.cat(list_x_row)
        list_x.append(x_row)
    x_packed = torch.cat(list_x).data.clone()
    x_packed.requires_grad = True
    batch_offset = torch.cumsum(self.f_len * (self.y_len+1), dim=0)
    return x_packed, batch_offset