diff --git a/tensorflow/python/distribute/tpu_values.py b/tensorflow/python/distribute/tpu_values.py
index 5ac2a11f82b..9d0719f34b4 100644
--- a/tensorflow/python/distribute/tpu_values.py
+++ b/tensorflow/python/distribute/tpu_values.py
@@ -112,7 +112,7 @@ class TPUVariableMixin(object):
   def handle(self):
     # If we're in a tpu.rewrite(), return the replicated handle.
     tpu_context = enclosing_tpu_context()
-    if tpu_context is None:
+    if tpu_context is None or context.executing_eagerly():
       return self._get_closest().handle
     else:
       return tpu_context.get_replicated_var_handle(self._handle_id,
diff --git a/tensorflow/python/distribute/values_test.py b/tensorflow/python/distribute/values_test.py
index 0c7b3dffd2b..685dbaf4d40 100644
--- a/tensorflow/python/distribute/values_test.py
+++ b/tensorflow/python/distribute/values_test.py
@@ -990,6 +990,39 @@ class MirroredVariableTest(test.TestCase, parameterized.TestCase):
         array_ops.zeros(distribution.num_replicas_in_sync, dtypes.float32),
         per_replica_results)
 
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
+              strategy_combinations.tpu_strategy,
+              strategy_combinations.central_storage_strategy_with_two_gpus,
+          ],
+          mode=["eager"]))
+  def testInitScope(self, distribution):
+
+    class C(object):
+      pass
+
+    obj = C()
+    obj.w = None
+    obj.v = None
+
+    @def_function.function
+    def assign():
+      with ops.init_scope():
+        if obj.w is None:
+          obj.w = variables_lib.Variable(
+              0, aggregation=variables_lib.VariableAggregation.MEAN)
+          obj.v = variables_lib.Variable(
+              obj.w.read_value(),
+              aggregation=variables_lib.VariableAggregation.MEAN)
+
+      return obj.v.assign_add(2)
+
+    per_replica_results = self.evaluate(
+        distribution.experimental_local_results(distribution.run(assign)))
+    self.assertAllEqual([2, 2], per_replica_results)
+
   @combinations.generate(
       combinations.combine(
           distribution=[