class_docstr: Base QuantizationTestCase for PT2E QAT with some helper methods.
class_docstr_tok: ["Base","QuantizationTestCase","for","PT2E","QAT","with","some","helper","methods","."]
docstr: Return an instance of a simple test model containing the
  conv[-bn][-relu] pattern. By default, this returns a
  conv-bn model with conv bias.
docstr_tok: ["Return","an","instance","of","a","simple","test","model","containing","the","conv","[","-bn","]","[","-relu","]","pattern",".","By","default",",","this","returns","a","conv-bn","model","with","conv","bias","."]
code: def _get_conv_bn_model(
      self,
      has_conv_bias: bool = True,
      has_bn: bool = True,
      has_relu: bool = False,
      **conv_kwargs,
  ):
      """
      Return an instance of a simple test model containing the
      conv[-bn][-relu] pattern. By default, this returns a
      conv-bn model with conv bias.
      """
      return self._BaseConvBnModel(
          self.conv_class,
          self.bn_class,
          has_conv_bias,
          has_bn,
          has_relu,
          **conv_kwargs,
      )
  
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 PT2EQATTestCase obj's underlying member method code with example usage: obj._get_conv_bn_model(has_conv_bias, has_bn, has_relu) and returns: self