class_docstr: Class to quantize the given floating-point values using symmetric quantization with given range and bitwidth.
class_docstr_tok: ["Class","to","quantize","the","given","floating-point","values","using","symmetric","quantization","with","given","range","and","bitwidth","."]
code: def backward(ctx, grad_output):
      scale = ctx.scale
      if len(grad_output.shape) == 4:
          scale = scale.view(-1, 1, 1, 1)
      # reshape scale and zeropoint for linear weights
      elif len(grad_output.shape) == 2:
          scale = scale.view(-1, 1)
      else:
          scale = scale.view(-1)
  
      return grad_output.clone() / scale, None, None, None, None
  
imports: import decimal
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Function
  from ...utils import logging
label_desc: Use image node_id 2 for calling the SymmetricQuantFunction obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: unknown,  None,  None,  None,  None