code: def test_transform_for_annotation(self):
      class TestQuantizer(Quantizer):
          def transform_for_annotation(
              self, model: torch.fx.GraphModule
          ) -> torch.fx.GraphModule:
              for n in model.graph.nodes:
                  if n.target == torch.ops.aten.add.Tensor:
                      n.target = torch.ops.aten.mul.Tensor
              return model
  
          def annotate(
              self, model: torch.fx.GraphModule
          ) -> torch.fx.GraphModule:
              return model
  
          def validate(self, model: torch.fx.GraphModule) -> None:
              pass
  
      class M(torch.nn.Module):
          def forward(self, x):
              return x + 3
  
      m = M().eval()
      quantizer = TestQuantizer()
      example_inputs = (torch.randn(1, 2, 3, 3),)
      m = capture_pre_autograd_graph(m, example_inputs)
      m = prepare_pt2e(m, quantizer)
      m(*example_inputs)
      node_occurrence = {
          ns.call_function(torch.ops.aten.add.Tensor): 0,
          ns.call_function(torch.ops.aten.mul.Tensor): 1,
      }
      self.checkGraphModuleNodes(
          m, expected_node_occurrence=node_occurrence
      )
  
imports: from typing import List, Tuple
  import torch
  from torch._export import capture_pre_autograd_graph
  from torch import Tensor
  from torch.ao.quantization import observer, ObserverOrFakeQuantize, QConfigMapping
  from torch.ao.quantization.quantizer import DerivedQuantizationSpec, FixedQParamsQuantizationSpec, QuantizationAnnotation, QuantizationSpec, Quantizer, SharedQuantizationSpec
  from torch.ao.quantization.quantizer.xnnpack_quantizer import XNNPACKQuantizer, get_symmetric_quantization_config
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import OP_TO_ANNOTATOR, QuantizationConfig
  from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer
  from torch.ao.quantization.quantizer.embedding_quantizer import EmbeddingQuantizer
  from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e, prepare_qat_pt2e
  from torch.ao.quantization.qconfig import default_per_channel_symmetric_qnnpack_qconfig, float_qparams_weight_only_qconfig, per_channel_weight_observer_range_neg_127_to_127, QConfig, weight_observer_range_neg_127_to_127
  from torch.fx import Node
  from torch.testing._internal.common_quantization import NodeSpec, PT2EQuantizationTestCase, skipIfNoQNNPACK, TestHelperModules
  from torch.testing._internal.common_utils import TemporaryFileName
label_desc: Use image node_id 20 for calling the TestQuantizePT2E obj's underlying member method code with example usage: obj.test_transform_for_annotation() and returns: model, model, unknown