code: def quant_backward(
      tensor, grad_output, quant_type, scale, zero_point, qmin, qmax
  ):
      if quant_type == QuantType.QUANT_OUTPUT:
          grad_output[torch.abs(tensor) > 1] = 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 ClipGrad 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