class_docstr: Base quantizer for pytorch quantizer
docstr: quantize should overload this method to quantize output.
  This method is effectively hooked to :meth:`forward` of the model.
  Parameters
  ----------
  output : Tensor
      output that needs to be quantized
  wrapper : QuantizerModuleWrapper
      the wrapper for origin module
docstr_tok: ["quantize","should","overload","this","method","to","quantize","output",".","This","method","is","effectively","hooked","to",":","meth",":","`","forward","`","of","the","model",".","Parameters","--","--","--","--","--","output",":","Tensor","output","that","needs","to","be","quantized","wrapper",":","QuantizerModuleWrapper","the","wrapper","for","origin","module"]
code: def quantize_output(self, output, wrapper, **kwargs):
      """
      quantize should overload this method to quantize output.
      This method is effectively hooked to :meth:`forward` of the model.
      Parameters
      ----------
      output : Tensor
          output that needs to be quantized
      wrapper : QuantizerModuleWrapper
          the wrapper for origin module
      """
      raise NotImplementedError(
          "Quantizer must overload quantize_output()"
      )
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 3 for calling the Quantizer obj's underlying member method code with example usage: obj.quantize_output(output, wrapper) without return types