code: def testReplicaModeWithoutZeroDebias(self, distribution):
      replica_id = [0]
  
      def replica_fn():
          var = variables.Variable([10.0, 11.0])
          val = constant_op.constant(
              [1.0 + replica_id[0], 2.0 - replica_id[0]]
          )
          replica_id[0] += 1
          decay = 0.25
          assign = moving_averages.assign_moving_average(
              var, val, decay, zero_debias=False
          )
          return var, assign
  
      with distribution.scope():
          var, assign = distribution.extended.call_for_each_replica(
              replica_fn
          )
          self.evaluate(variables.global_variables_initializer())
          self.assertAllClose([10.0, 11.0], self.evaluate(var))
          self.evaluate(distribution.experimental_local_results(assign))
          # Mean of val across calls to replica_fn().
          average_val = [
              1.0 + 0.5 * (replica_id[0] - 1),
              2.0 - 0.5 * (replica_id[0] - 1),
          ]
          val_weight = 1.0 - 0.25
          self.assertAllClose(
              [
                  10.0 * 0.25 + average_val[0] * val_weight,
                  11.0 * 0.25 + average_val[1] * val_weight,
              ],
              self.evaluate(var),
          )
  
imports: from absl.testing import parameterized
  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.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.ops import variables
  from tensorflow.python.training import moving_averages
label_desc: Use image node_id 1 for calling the AssignMovingAveragesTest obj's underlying member method code with example usage: obj.testReplicaModeWithoutZeroDebias(distribution) and returns: var,  assign