docstr: Restore to variables with mirroring in a fresh graph.
code: def _restore_mirrored(self, save_path, distribution):
      """Restore to variables with mirroring in a fresh graph."""
      with self.session(graph=ops.Graph()) as sess:
          mirrored = _make_mirrored(distribution)
          v = mirrored.values
  
          # Overwrite the initial values.
          self._assign_mirrored(mirrored, [7.0, 8.0])
  
          # Restores the saved value of 3. to both variables.
          saver = saver_lib.Saver(var_list=[mirrored])
          saver.restore(sess, save_path)
          self.assertEqual([3.0, 3.0], self.evaluate([v[0], v[1]]))
  
imports: import os
  from absl.testing import parameterized
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import strategy_test_lib
  from tensorflow.python.distribute import test_util
  from tensorflow.python.distribute import tpu_values
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import context
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.training import saver
label_desc: Use image node_id 7 for calling the MirroredVariableSaveRestoreTest obj's underlying member method code with example usage: obj._restore_mirrored(save_path, distribution) without return types