I am very happy to receive your reply. I extracted the trained bias value and replaced the bias value with the rebp function. Then test the network comparison accuracy, and found that bias does not affect the change of precision, and after the replacement of the bias, print out the float type data, not the Tensor type.
        bias_name = 'layer' + str(id + 1) + '.bias'#提取bias值
        layer_bias_upper = list(state_dict[bias_name])#存入列表layer_bias__upper

#        print(layer_bias_upper)

        if   p==0:
             numn=20
        elif p==1:
             numn=20
        elif p==2:
             numn=20
        else:
             numn=10

        for i,value2 in enumerate(range(0,numn)):
#            if i < 2:
               #print('第几个神经元')
               #print(i)
               print(layer_bias_upper[i])


               adder = CDLL('rank-b/b.so')
               d1 = c_double(layer_bias_upper[i])
               repb = adder.repb

               repb.restype = c_double

               layer_bias_upper[i]=repb(p,i,d1) #The repb function returns a float value and assigns it to bias
         dict_layer_bias_upper = {bias_name: layer_bias_upper}
         state_dict.update(dict_layer_bias_upper)```