docstr: Test to make sure the quantized model gets quantized weight (quantize_per_channel op is folded)
          
docstr_tok: ["Test","to","make","sure","the","quantized","model","gets","quantized","weight","(","quantize_per_channel","op","is","folded",")"]
code: def test_fold_quantize_per_channel(self):
      """Test to make sure the quantized model gets quantized weight (quantize_per_channel op is folded)"""
      m = self._get_pt2e_quantized_linear(is_per_channel=True)
      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_channel.default
          ): 1,
          ns.call_function(
              torch.ops.quantized_decomposed.dequantize_per_tensor.default
          ): 2,
      }
      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 14 for calling the TestQuantizePT2E obj's underlying member method code with example usage: obj.test_fold_quantize_per_channel() without return types