code: def from_observed(cls, other):
      converted = torch.ao.quantization.convert(
          other,
          mapping=None,
          inplace=False,
          remove_qconfig=True,
          convert_custom_config_dict=None,
      )
      converted.__class__ = cls
      # Remove the parameters for the bias_k and bias_v to quantize them
      # TODO: This is a potential source of accuracy drop.
      #       quantized cat takes the scale and zp of the first
      #       element, which might lose the precision in the bias_k
      #       and the bias_v (which are cat'ed with k/v being first).
      if converted.bias_k is not None:
          bias_k = converted._parameters.pop("bias_k")
          sc, zp = torch._choose_qparams_per_tensor(
              bias_k, reduce_range=False
          )
          bias_k = torch.quantize_per_tensor(
              bias_k, sc, zp, torch.quint8
          )
          setattr(converted, "bias_k", bias_k)  # noqa: B010
  
      if converted.bias_v is not None:
          bias_v = converted._parameters.pop("bias_v")
          sc, zp = torch._choose_qparams_per_tensor(
              bias_k, reduce_range=False
          )
          bias_v = torch.quantize_per_tensor(
              bias_v, sc, zp, torch.quint8
          )
          setattr(converted, "bias_v", bias_v)  # noqa: B010
  
      del converted.in_proj_weight
      del converted.in_proj_bias
  
      return converted
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 3 for calling the MultiheadAttention obj's underlying member method code with example usage: obj.from_observed(cls, other) and returns: converted