docstr: Restore to variables without mirroring in a fresh graph.
code: def _restore_normal(self, save_path):
      """Restore to variables without mirroring in a fresh graph."""
      with self.session(graph=ops.Graph()) as sess:
          var = variable_scope.get_variable(
              name="v", initializer=7.0, use_resource=True
          )
  
          # Overwrite the initial value.
          self.evaluate(var.assign(8.0))
  
          # Restores the saved value of 3. to `var`.
          saver = saver_lib.Saver(var_list=[var])
          saver.restore(sess, save_path)
          self.assertEqual(3.0, self.evaluate(var))
  
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 6 for calling the MirroredVariableSaveRestoreTest obj's underlying member method code with example usage: obj._restore_normal(save_path) without return types