code: def forward(self, *inputs):
      if "input" in self.config["quant_types"]:
          inputs = self.quantizer.quant_grad.apply(
              inputs, QuantType.QUANT_INPUT, self
          )
  
      if "weight" in self.config["quant_types"] and _check_weight(
          self.module
      ):
          self.quantizer.quant_grad.apply(
              self.module.old_weight, QuantType.QUANT_WEIGHT, self
          )
          result = self.module(*inputs)
      else:
          result = self.module(*inputs)
  
      if "output" in self.config["quant_types"]:
          result = self.quantizer.quant_grad.apply(
              result, QuantType.QUANT_OUTPUT, self
          )
      return result
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 2 for calling the QuantizerModuleWrapper obj's underlying member method code with example usage: obj.forward() and returns: result