code: def quant_backward(
      tensor, grad_output, quant_type, scale, zero_point, qmin, qmax
  ):
      tensor_q = QuantGrad._quantize(tensor, scale, zero_point)
      mask = (tensor_q < qmin) | (tensor_q > qmax)
      grad_output[mask] = 0
      return grad_output
  
imports: import logging
  import copy
  import torch
  from schema import Schema, And, Or, Optional
  from ..utils.config_validation import CompressorSchema
  from ..compressor import Quantizer, QuantGrad, QuantType
label_desc: Use image node_id 1 for calling the QATGrad 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