ok. Got it. Thank you for replying. It has solved my problem.
One last thing, do I have to create all summation operation unique in for loop?
For example,
        resl = self.q_add00.add(resl, z0)
        resl = self.q_add01.add(resl, z1)   
        resl = self.q_add02.add(resl, z2)

If I do like above mentioned, that part of the model will look something like:
      (q_add00): QFunctional(
        scale=1.027651309967041, zero_point=67
        (activation_post_process): Identity()
      )
      (q_add01): QFunctional(
        scale=1.0117942094802856, zero_point=68
        (activation_post_process): Identity()
      )
      (q_add02): QFunctional(
        scale=0.9806106686592102, zero_point=74
        (activation_post_process): Identity()
      )

I do not know what is wrong but the quantized model has 0 %  accuracy. That is why I am trying different approaches.
Quantized aware training can be one solution. But, what are the other parameters/procedures to check/apply in Post-training static quantization for better accuracy for ResNet-50 backend models?
I thank you in advance.