docstr: Make sure the constant propagation does not apply to things unrelated to
  quantization
docstr_tok: ["Make","sure","the","constant","propagation","does","not","apply","to","things","unrelated","to","quantization"]
code: def test_dont_fold_other_constant(self):
      """Make sure the constant propagation does not apply to things unrelated to
      quantization
      """
  
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear = torch.nn.Linear(2, 2)
              self.dont_fold_me = torch.nn.Parameter(torch.randn(2, 2))
  
          def forward(self, x):
              t = self.dont_fold_me.t()
              return self.linear(x) + t
  
      quantizer = XNNPACKQuantizer()
      operator_config = get_symmetric_quantization_config(
          is_per_channel=False
      )
      # only quantize linear, so add is not quantized and the constant Tensor
      # should not be folded
      quantizer.set_module_type(torch.nn.Linear, operator_config)
      example_inputs = (torch.randn(2, 2),)
      m = M().eval()
      m = self._quantize(m, quantizer, example_inputs)
      node_occurrence = {
          # quantize op for weight node is folded
          ns.call_function(
              torch.ops.quantized_decomposed.quantize_per_tensor.default
          ): 2,
          ns.call_function(
              torch.ops.quantized_decomposed.dequantize_per_tensor.default
          ): 3,
          # transpose op not folded
          ns.call_function(torch.ops.aten.t.default): 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 15 for calling the TestQuantizePT2E obj's underlying member method code with example usage: obj.test_dont_fold_other_constant() and returns: unknown