code: def __init__(
      self,
      # ConvNd args
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      bias=None,
      padding_mode="zeros",
      # BatchNorm2d args
      # num_features: out_channels
      eps=1e-05,
      momentum=0.1,
      # affine: True
      # track_running_stats: True
      # Args for this module
      freeze_bn=False,
      qconfig=None,
  ):
      kernel_size = _pair(kernel_size)
      stride = _pair(stride)
      padding = _pair(padding)
      dilation = _pair(dilation)
      _ReferenceConvBnNd.__init__(
          self,
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          False,
          _pair(0),
          groups,
          bias,
          padding_mode,
          eps,
          momentum,
          freeze_bn,
          qconfig,
      )
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.backends.mkldnn
  from torch.nn import Conv2d, BatchNorm2d, ReLU, init
  from torch.ao.nn.intrinsic.qat import ConvBn2d, ConvBnReLU2d
  from torch.nn.modules.utils import _pair
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.dynamic
  import torch.ao.nn.qat
  import torch.ao.nn.intrinsic.qat
  import torch.ao.nn.qat.dynamic
  from torch.ao.quantization import prepare, convert, prepare_qat, quantize_qat, QuantStub, DeQuantStub, default_qconfig, default_qat_qconfig, default_embedding_qat_qconfig, default_symmetric_qnnpack_qat_qconfig, get_default_qat_qconfig, FixedQParamsFakeQuantize, FusedMovingAvgObsFakeQuantize, get_embedding_qat_module_mappings, get_embedding_static_quant_module_mappings, NoopObserver
  from torch.ao.quantization.qconfig import qconfig_equals
  from torch.testing._internal.common_quantization import DeFusedEmbeddingBagLinear, QuantizationTestCase, QuantStubModel, ManualLinearQATModel, ManualDropoutQATModel, ManualLinearDynamicQATModel, ManualConvLinearQATModel, ManualConvLinearSymmQATModel, ManualEmbeddingBagLinear, TwoLayerLinearModel, test_only_eval_fn, test_only_train_fn
  from torch.testing._internal.common_quantized import override_quantized_engine, supported_qengines, override_qengines
  from torch.testing._internal.common_utils import skipIfNoXNNPACK
  from hypothesis import given
  from hypothesis import strategies
  import torch.testing._internal.hypothesis_utils
  from functools import reduce
label_desc: Use image node_id 1 to create a new _ReferenceConvBn2d object from inherited base classes: _ReferenceConvBnNd, nn with example: obj = _ReferenceConvBn2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, eps, momentum, freeze_bn, qconfig)