code: def doTestExportNestedNames(self, use_resource=False):
      graph1 = ops.Graph()
      with graph1.as_default():
          with ops.name_scope("hidden1/hidden2/hidden3"):
              images = constant_op.constant(
                  1.0, dtypes.float32, shape=[3, 2], name="images"
              )
              if use_resource:
                  weights1 = variables.Variable(
                      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], name="weights"
                  )
                  biases1 = resource_variable_ops.ResourceVariable(
                      [0.1] * 3, name="biases"
                  )
              else:
                  biases1 = variables.Variable([0.1] * 3, name="biases")
                  weights1 = variables.Variable(
                      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], name="weights"
                  )
              nn_ops.relu(
                  math_ops.matmul(images, weights1) + biases1,
                  name="relu",
              )
  
      orig_meta_graph, var_list = meta_graph.export_scoped_meta_graph(
          export_scope="hidden1/hidden2", graph=graph1
      )
      var_names = [v.name for _, v in var_list.items()]
      self.assertEqual(
          ["hidden3/biases:0", "hidden3/weights:0"],
          sorted(var_list.keys()),
      )
      self.assertEqual(
          [
              "hidden1/hidden2/hidden3/biases:0",
              "hidden1/hidden2/hidden3/weights:0",
          ],
          sorted(var_names),
      )
      for node in orig_meta_graph.graph_def.node:
          self.assertTrue(node.name.startswith("hidden3"))
  
      graph2 = ops.Graph()
      new_var_list = meta_graph.import_scoped_meta_graph(
          orig_meta_graph,
          import_scope="new_hidden1/new_hidden2",
          graph=graph2,
      )
      self.assertEqual(
          ["hidden3/biases:0", "hidden3/weights:0"],
          sorted(new_var_list.keys()),
      )
      new_var_names = [v.name for _, v in new_var_list.items()]
      self.assertEqual(
          [
              "new_hidden1/new_hidden2/hidden3/biases:0",
              "new_hidden1/new_hidden2/hidden3/weights:0",
          ],
          sorted(new_var_names),
      )
  
      nodes = [
          "new_hidden1/new_hidden2/hidden3/biases/Assign",
          "new_hidden1/new_hidden2/hidden3/weights/Assign",
      ]
      expected = [
          b"loc:@new_hidden1/new_hidden2/hidden3/biases",
          b"loc:@new_hidden1/new_hidden2/hidden3/weights",
      ]
  
imports: import math
  import os.path
  import random
  import shutil
  from tensorflow.core.framework import graph_pb2
  from tensorflow.core.protobuf import meta_graph_pb2
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import meta_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import partitioned_variables
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import gfile
  from tensorflow.python.platform import test
  from tensorflow.python.training import queue_runner_impl
label_desc: Use image node_id 13 for calling the ScopedMetaGraphTest obj's underlying member method code with example usage: obj.doTestExportNestedNames(use_resource) without return types