class_docstr: Base class for overriding backward function of quantization operation.
docstr: Reference function for quantizing x -- non-clamped.
  Parameters
  ----------
  x : Tensor
      tensor to be quantized
  scale : Tensor
      scale for quantizing x
  zero_point : Tensor
      zero_point for quantizing x
  Returns
  -------
  tensor
      quantized x without clamped
docstr_tok: ["Reference","function","for","quantizing","x","--","non-clamped",".","Parameters","--","--","--","--","--","x",":","Tensor","tensor","to","be","quantized","scale",":","Tensor","scale","for","quantizing","x","zero_point",":","Tensor","zero_point","for","quantizing","x","Returns","--","--","--","-","tensor","quantized","x","without","clamped"]
code: def _quantize(cls, x, scale, zero_point):
      """
      Reference function for quantizing x -- non-clamped.
      Parameters
      ----------
      x : Tensor
          tensor to be quantized
      scale : Tensor
          scale for quantizing x
      zero_point : Tensor
          zero_point for quantizing x
      Returns
      -------
      tensor
          quantized x without clamped
      """
      return ((x / scale) + zero_point).round()
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 1 for calling the QuantGrad obj's underlying member method code with example usage: obj._quantize(cls, x, scale, zero_point) and returns: unknown