class_docstr: Dummy quantizer that annotates conv bn in such a way that the bias qparams are
  derived from the conv input activation and weight qparams.
class_docstr_tok: ["Dummy","quantizer","that","annotates","conv","bn","in","such","a","way","that","the","bias","qparams","are","derived","from","the","conv","input","activation","and","weight","qparams","."]
code: def _derive_bias_qparams_from_act_and_weight_qparams(
      self, obs_or_fqs
  ):
      act_scale, _ = obs_or_fqs[0].calculate_qparams()
      weight_scale, _ = obs_or_fqs[1].calculate_qparams()
      bias_scale = torch.tensor(
          [act_scale * weight_scale], dtype=torch.float32
      )
      bias_zero_point = torch.tensor([0], dtype=torch.int32)
      return bias_scale, bias_zero_point
  
imports: import copy
  import operator
  import unittest
  from typing import Any, Optional, Tuple, Type
  import torch
  from torch._export import capture_pre_autograd_graph
  from torch.ao.quantization import default_fake_quant, FusedMovingAvgObsFakeQuantize, MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver, QConfigMapping
  from torch.ao.quantization.backend_config import get_qnnpack_backend_config
  from torch.ao.quantization.qconfig import default_per_channel_symmetric_qnnpack_qat_qconfig, default_symmetric_qnnpack_qat_qconfig
  from torch.ao.quantization.quantize_fx import prepare_qat_fx
  from torch.ao.quantization.quantize_pt2e import _convert_to_reference_decomposed_fx, convert_pt2e, prepare_pt2e, prepare_qat_pt2e
  from torch.ao.quantization.quantizer import DerivedQuantizationSpec, QuantizationAnnotation, QuantizationSpec, Quantizer
  from torch.ao.quantization.quantizer.xnnpack_quantizer import get_symmetric_quantization_config, XNNPACKQuantizer
  from torch.testing._internal.common_cuda import TEST_CUDA
  from torch.testing._internal.common_quantization import NodeSpec, QuantizationTestCase, skip_if_no_torchvision, skipIfNoQNNPACK
  from torch.testing._internal.common_quantized import override_quantized_engine
label_desc: Use image node_id 1 for calling the ConvBnDerivedBiasQuantizer obj's underlying member method code with example usage: obj._derive_bias_qparams_from_act_and_weight_qparams(obs_or_fqs) and returns: bias_scale,  bias_zero_point