code: def register_hook(self):
      # forward hook
      def quantize_hook(module, x, y):
          # update parameters every 1000 iterations
          if self.counter % self.update_step == 0:
              self.scale = None
              self.zero_point = None
          self.counter += 1
  
          # train with QuantNoise and evaluate the fully quantized network
          p = self.p if self.module.training else 1
  
          # quantize activations
          y_q, self.scale, self.zero_point = emulate_int(
              y.detach(),
              bits=self.bits,
              method=self.method,
              scale=self.scale,
              zero_point=self.zero_point,
          )
  
          # mask to apply noise
          mask = torch.zeros_like(y)
          mask.bernoulli_(1 - p)
          noise = (y_q - y).masked_fill(mask.bool(), 0)
  
          # using straight-through estimator (STE)
          clamp_low = -self.scale * self.zero_point
          clamp_high = self.scale * (
              2**self.bits - 1 - self.zero_point
          )
          return (
              torch.clamp(y, clamp_low.item(), clamp_high.item())
              + noise.detach()
          )
  
      # register hook
      self.handle = self.module.register_forward_hook(quantize_hook)
  
imports: import torch
  from ..ops import emulate_int
label_desc: Use image node_id 2 for calling the ActivationQuantizer obj's underlying member method code with example usage: obj.register_hook() and returns: unknown