code: def test_conv_linear_no_permute(self):
      quantizer = XNNPACKQuantizer()
      quantization_config = get_symmetric_quantization_config(
          is_per_channel=True
      )
      quantizer.set_global(quantization_config)
      node_occurrence = {
          # input and output are using quantize_per_tensor and weight is using quantize_per_channel
          torch.ops.quantized_decomposed.quantize_per_tensor.default: 5,
          torch.ops.quantized_decomposed.dequantize_per_tensor.default: 5,
          # quantize_per_channel for weights are const propagated
          torch.ops.quantized_decomposed.quantize_per_channel.default: 0,
          torch.ops.quantized_decomposed.dequantize_per_channel.default: 3,
      }
      qconfig = default_per_channel_symmetric_qnnpack_qconfig
      qconfig_mapping = QConfigMapping().set_global(qconfig)
      # Test with 2d inputs
      example_inputs = (torch.randn(2, 3, 4, 4),)
      self._test_quantizer(
          TestHelperModules.Conv2dWithTwoLinear(),
          example_inputs,
          quantizer,
          node_occurrence,
          [],
          True,
          qconfig_mapping,
      )
  
imports: import copy
  import operator
  import torch
  import torch._dynamo
  from torch._export import capture_pre_autograd_graph
  from torch.ao.ns.fx.utils import compute_sqnr
  from torch.ao.quantization import default_dynamic_fake_quant, default_dynamic_qconfig, observer, QConfig, QConfigMapping
  from torch.ao.quantization.backend_config import get_qnnpack_backend_config
  from torch.ao.quantization.qconfig import default_per_channel_symmetric_qnnpack_qconfig, default_symmetric_qnnpack_qconfig, per_channel_weight_observer_range_neg_127_to_127, weight_observer_range_neg_127_to_127
  from torch.ao.quantization.quantize_fx import _convert_to_reference_decomposed_fx, convert_to_reference_fx, prepare_fx
  from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
  from torch.ao.quantization.quantizer.xnnpack_quantizer import get_symmetric_quantization_config, XNNPACKQuantizer
  from torch.testing._internal.common_quantization import NodeSpec, PT2EQuantizationTestCase, skip_if_no_torchvision, skipIfNoQNNPACK, TestHelperModules
  from torch.testing._internal.common_quantized import override_quantized_engine
label_desc: Use image node_id 6 for calling the TestXNNPACKQuantizer obj's underlying member method code with example usage: obj.test_conv_linear_no_permute() without return types