class_docstr: Base TestCase to be used for all conv-bn[-relu] fusion patterns.
class_docstr_tok: ["Base","TestCase","to","be","used","for","all","conv-bn","[","-relu","]","fusion","patterns","."]
code: def setUp(self):
      # NB: Skip the test if this is a base class, this is to handle the test
      # discovery logic in buck which finds and runs all tests here including
      # the base class which we don't want to run
      if self.id() and "_Base" in self.id():
          self.skipTest("Skipping test running from base class")
  
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 TestQuantizePT2EQAT_ConvBn_Base obj's underlying member method code with example usage: obj.setUp() without return types