code: def _test_move_exported_model_to_eval_dropout(self, inplace=False):
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.dropout = torch.nn.Dropout(0.5, inplace=inplace)
  
          def forward(self, x):
              return self.dropout(x)
  
      example_inputs = (torch.randn(1),)
      m = M().train()
      m = capture_pre_autograd_graph(m, example_inputs)
  
      # Assert that dropout op exists and is in train mode
      dropout_node = None
      for n in m.graph.nodes:
          if (
              n.target == torch.ops.aten.native_dropout.default
              or n.target == torch.ops.aten.dropout_.default
          ):
              dropout_node = n
              break
      self.assertTrue(dropout_node is not None)
      self.assertTrue(dropout_node.args[2])
  
      # Do the subgraph rewriting
      torch.ao.quantization.move_exported_model_to_eval(m)
  
      # Assert that dropout op is now replaced with a clone op
      targets = [n.target for n in m.graph.nodes]
      if inplace:
          dropout_eval_node = None
          for node in m.graph.nodes:
              if node.target == torch.ops.aten.dropout_.default:
                  dropout_eval_node = node
          self.assertTrue(dropout_eval_node is not None)
          self.assertFalse(dropout_eval_node.args[2])
      else:
          self.assertTrue(torch.ops.aten.clone.default in targets)
          self.assertTrue(
              torch.ops.aten.native_dropout.default not in targets
          )
  
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 24 for calling the TestQuantizePT2E obj's underlying member method code with example usage: obj._test_move_exported_model_to_eval_dropout(inplace) and returns: self