Clean up unnecessary code in PY3

PiperOrigin-RevId: 361348924

official/modeling/optimization/ema_optimizer.py

@@ -70,7 +70,7 @@ class ExponentialMovingAverage(tf.keras.optimizers.Optimizer):
       **kwargs: keyword arguments. Allowed to be {`clipnorm`,
         `clipvalue`, `lr`, `decay`}.
     """
-    super(ExponentialMovingAverage, self).__init__(name, **kwargs)
+    super().__init__(name, **kwargs)
     self._average_decay = average_decay
     self._start_step = tf.constant(start_step, tf.float32)
     self._dynamic_decay = dynamic_decay

official/modeling/optimization/lr_schedule.py

@@ -44,7 +44,7 @@ class LinearWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):
       warmup_learning_rate: Initial learning rate for the warmup.
       name: Optional, name of warmup schedule.
     """
-    super(LinearWarmup, self).__init__()
+    super().__init__()
     self._name = name
     self._after_warmup_lr_sched = after_warmup_lr_sched
     self._warmup_steps = warmup_steps
@@ -101,7 +101,7 @@ class PolynomialWarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
                warmup_steps: int,
                power: float = 1.0,
                name: str = "PolynomialWarmup"):
-    super(PolynomialWarmUp, self).__init__()
+    super().__init__()
     if isinstance(after_warmup_lr_sched,
                   tf.keras.optimizers.schedules.LearningRateSchedule):
       self._initial_learning_rate = after_warmup_lr_sched(warmup_steps)
@@ -174,7 +174,7 @@ class DirectPowerDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
       power: The order of the polynomial.
       name: Optional, name of warmup schedule.
     """
-    super(DirectPowerDecay, self).__init__()
+    super().__init__()
     self._initial_learning_rate = initial_learning_rate
     self._power = power
     self._name = name
@@ -222,7 +222,7 @@ class PowerAndLinearDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
         the learning rate will be multiplied by a linear decay.
       name: Optional, name of warmup schedule.
     """
-    super(PowerAndLinearDecay, self).__init__()
+    super().__init__()
     self._initial_learning_rate = initial_learning_rate
     self._total_decay_steps = total_decay_steps
     self._power = power
@@ -276,7 +276,7 @@ class PowerDecayWithOffset(tf.keras.optimizers.schedules.LearningRateSchedule):
       pre_offset_learning_rate: The maximum learning rate we'll use.
       name: Optional, name of warmup schedule.
     """
-    super(PowerDecayWithOffset, self).__init__()
+    super().__init__()
     self._initial_learning_rate = initial_learning_rate
     self._power = power
     self._offset = offset

official/modeling/optimization/optimizer_factory.py

@@ -49,7 +49,7 @@ WARMUP_CLS = {
 }
 
 
-class OptimizerFactory(object):
+class OptimizerFactory:
   """Optimizer factory class.
 
   This class builds learning rate and optimizer based on an optimization config.