message: Properly rename _apply_gradients functions
diff: @@ -170,7 +170,7 @@ class LossScaleOptimizer(optimizer.Optimizer):
  # TODO(nluehr) cleanup GraphKeys.TRAIN_OP
  return replica_context.merge_call(
  - self._maybe_apply_gradients_cross_replica,
  + self._distributed_apply,
  args=(grads_and_vars, global_step, name))
  def _distributed_apply(self,
  @@ -197,28 +197,19 @@ class LossScaleOptimizer(optimizer.Optimizer):
  replicas. If `global_step` was not None, that operation also
  increments `global_step`
  """
  - self._maybe_apply_gradients_cross_replica(distribution, grads_and_vars,
  - global_step, name)
  -
  - def _maybe_apply_gradients_cross_replica(self, distribution, grads_and_vars,
  - global_step, name):
  - """Conditionally apply gradients in cross replica context."""
  name = name if name is not None else self.get_name()
  grads = [g for g, _ in grads_and_vars]
  loss_scale_update_op, should_apply_grads = (
  self._loss_scale.update(grads))
  maybe_apply_op = smart_cond.smart_cond(
  should_apply_grads,
  - lambda: self._apply_gradients_cross_replica(distribution,
  - grads_and_vars,
  - global_step,
  - name+'-wrapped'),
  + lambda: self._apply_gradients(distribution, grads_and_vars,
  + global_step, name+'-wrapped'),
  control_flow_ops.no_op)
  return control_flow_ops.group(maybe_apply_op, loss_scale_update_op,
  name=name)
  - def _apply_gradients_cross_replica(self, distribution, grads_and_vars,
  - global_step, name):
  + def _apply_gradients(self, distribution, grads_and_vars, global_step, name):
  """Unconditionally apply gradients in cross replica context."""
  update_ops = distribution.extended.call_for_each_replica(
  self._optimizer.apply_gradients,
  