What is the best/fastest way to implement for-loop in MXNet?

1

I try to implement SRU in MXNet, and I find it is difficult to implement a for-loop.
Here is what I have done, but the question is, for i in ndarray and for i in sym behaves differently, thus the code not working.

import mxnet as mx
class SRU(mx.gluon.HybridBlock):
    def __init__(self,hidden_size,use_bias=True,layout='TNC',activation=None,params=None,prefix=None, **kwargs):
        assert layout == 'TNC' , "TNC support only, use swapaxes to change the layout first."
        assert activation==None, "You should manually add activaion layers rather than specific it here."
        super(SRU, self).__init__(params=params,prefix=prefix,**kwargs)
        with self.name_scope():
            self.W=mx.sym.var(self.prefix+"_W_weight",shape=(hidden_size,hidden_size),init=mx.init.Xavier())
            self.Wfr=mx.sym.var(self.prefix+"_W_fr_weight",shape=(hidden_size,hidden_size*2),init=mx.init.Xavier())
            self.v=mx.sym.var(self.prefix+"_v_fr_weight",shape=(1,1,hidden_size*2),init=mx.init.Xavier())#shape is used for broadcast_add which actual shape is (batch_size,hidden_size*2)
            self.b=mx.sym.var(self.prefix+"_b_fr_weight",shape=(1,1,hidden_size*2),init=mx.init.Zero())
    def gen(self,F,Wx_plus_b_for_fr,Wx,x,c):
        for Wxpbt,Wxt,xt in zip(Wx_plus_b_for_fr,Wx,x):
            ft,rt=F.sigmoid(F.broadcast_add(Wxpbt,F.broadcast_mul(c.tile(2),self.v))).split(2,axis=0)
            c=F.broadcast_add(ft*c,(1-ft)*Wxt)
            yield F.broadcast_add(rt*c,(1-rt)*xt)
        self.c=c
    def hybrid_forward(self,F,x,c,*args,**params):
        Wx_plus_b_for_fr = F.broadcast_add(F.dot(x,self.Wfr),self.b)
        Wx = F.dot(x,self.W)
        ht_f = F.stack(*self.gen(F,Wx_plus_b_for_fr,Wx,x,c))
        return ht_f,self.c

I want to know what is the best solution for mxnet to implement a for-loop.
thanks.