code: def test_get_parent():
      class MyModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv2d = torch.nn.Conv2d(10, 20, kernel_size=3)
              self.bn2d = torch.nn.BatchNorm2d(20)
  
          def forward(self, x):
              x = self.conv2d(x)
              x = self.bn2d(x)
              return x
  
      model = MyModel()
      model.eval()
  
      traced_model = torch.fx.symbolic_trace(model)
  
      for node in traced_model.graph.nodes:
          if node.target == "conv2d":
              parent_name, _, name = node.target.rpartition(".")
              assert parent_name == ""
              parent_module = traced_model.get_submodule(parent_name)
              assert parent_module == traced_model
  
imports: import operator
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoModel, AutoModelForImageClassification, AutoTokenizer, BertModel
  from transformers.models.bert.modeling_bert import BertSelfAttention
  from transformers.utils.fx import symbolic_trace
  from optimum.fx.optimization import ChangeTrueDivToMulByInverse, FuseBatchNorm1dInLinear, FuseBatchNorm2dInConv2d, FuseBiasInLinear, MergeLinears, ReversibleTransformation, Transformation, compose
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 7 for calling a global function with example usage: test_get_parent() and returns: x