class_docstr: Supportive descriptor of quantization
  
  Describe how a tensor should be quantized. A QuantDescriptor and a tensor defines a quantized tensor.
  
  Args:
      num_bits: An integer. Number of bits of quantization. It is used to calculate scaling factor. Default 8.
      name: Seems a nice thing to have
  
  Keyword Arguments:
      fake_quant: A boolean. If True, use fake quantization mode. Default True.
      axis: None, int or tuple of int. axes which will have its own max for computing scaling factor.
          If None (the default), use per tensor scale.
          Must be in the range [-rank(input_tensor), rank(input_tensor)).
          e.g. For a KCRS weight tensor, quant_axis=(0) will yield per channel scaling.
          Default None.
      amax: A float or list/ndarray of floats of user specified absolute max range. If supplied,
          ignore quant_axis and use this to quantize. If learn_amax is True, will be used to initialize
          learnable amax. Default None.
      learn_amax: A boolean. If True, learn amax. Default False.
      scale_amax: A float. If supplied, multiply amax by scale_amax. Default None. It is useful for some
          quick experiment.
      calib_method: A string. One of ["max", "histogram"] indicates which calibration to use. Except the simple
          max calibration, other methods are all hisogram based. Default "max".
      unsigned: A Boolean. If True, use unsigned. Default False.
  
  Raises:
      TypeError: If unsupported type is passed in.
  
  Read-only properties:
      - fake_quant:
      - name:
      - learn_amax:
      - scale_amax:
      - axis:
      - calib_method:
      - num_bits:
      - amax:
      - unsigned:
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code: def unsigned(self):
      return self._unsigned
  
imports: import numpy
  import yaml
  import logging
  import torch
  from torch.autograd import Function
label_desc: Use image node_id 10 for calling the ScaledQuantDescriptor obj's underlying member method code with example usage: obj.unsigned() and returns: self