Apply name change(experimental_run_v2 -> run) for all callers in Tensorflow.

PiperOrigin-RevId: 301617216
Change-Id: I17624641a96dac369b52833d9c72c3d4d2172596

tensorflow/python/distribute/tpu_strategy_test.py

@@ -137,7 +137,7 @@ class TPUStrategyTest(test.TestCase):
       def computation():
         return random_ops.random_gamma([10], [0.5, 1.5])
 
-      return strategy.experimental_run_v2(computation)
+      return strategy.run(computation)
 
     with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                  "TPU compilation failed"):
@@ -149,7 +149,7 @@ class TPUStrategyTest(test.TestCase):
       def computation():
         return random_ops.random_normal([10])
 
-      return strategy.experimental_run_v2(computation)
+      return strategy.run(computation)
 
     good_run()
 

tensorflow/python/tpu/tpu.py

@@ -223,7 +223,7 @@ def tpu_replicated_input_resolver(op, resource_reads, resource_writes):
       return False
   # Replace tensors in `resource_inputs` which are outputs of TPUReplicatedInput
   # with the actual replicated inputs. This allows ACD to correct add control
-  # deps when there are multiple calls to `experimental_run_v2` in a
+  # deps when there are multiple calls to `run` in a
   # `tf.function`.
   def replace_with_unreplicated_resources(resource_inputs):
     """Replaces handles in `resource_inputs` with their unreplicated inputs."""

tensorflow/python/training/experimental/loss_scaling_gradient_tape.py

@@ -40,7 +40,7 @@ def _convert_to_per_replicas(distribution, values):
   Returns:
     `values`, but each element has been converted to a PerReplica value.
   """
-  return distribution.experimental_run_v2(
+  return distribution.run(
       lambda values: [array_ops.identity(v) for v in values],
       args=(values,)
   )