class_docstr: Base class for overriding backward function of quantization operation.
docstr: This method should be overrided by subclass to provide customized backward function,
  default implementation is Straight-Through Estimator
  Parameters
  ----------
  tensor : Tensor
      input of quantization operation
  grad_output : Tensor
      gradient of the output of quantization operation
  scale : Tensor
      the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`,
      you can define different behavior for different types.
  zero_point : Tensor
      zero_point for quantizing tensor
  qmin : Tensor
      quant_min for quantizing tensor
  qmax : Tensor
      quant_max for quantizng tensor
  Returns
  -------
  tensor
      gradient of the input of quantization operation
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code: def quant_backward(
      tensor, grad_output, quant_type, scale, zero_point, qmin, qmax
  ):
      """
      This method should be overrided by subclass to provide customized backward function,
      default implementation is Straight-Through Estimator
      Parameters
      ----------
      tensor : Tensor
          input of quantization operation
      grad_output : Tensor
          gradient of the output of quantization operation
      scale : Tensor
          the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`,
          you can define different behavior for different types.
      zero_point : Tensor
          zero_point for quantizing tensor
      qmin : Tensor
          quant_min for quantizing tensor
      qmax : Tensor
          quant_max for quantizng tensor
      Returns
      -------
      tensor
          gradient of the input of quantization operation
      """
      return grad_output
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 3 for calling the QuantGrad obj's underlying member method code with example usage: obj.quant_backward(tensor, grad_output, quant_type, scale, zero_point, qmin, qmax) and returns: grad_output