message: Update function names for V2 consistency
diff: @@ -129,10 +129,10 @@ class LossScaleOptimizer(optimizer.Optimizer):
  def _scale_grads(self, grads):
  loss_scale = self._loss_scale()
  loss_scale_reciprical = 1 / loss_scale
  - return [None if g is None else self._indexed_slices(
  + return [None if g is None else self._scale_grad(
  g, loss_scale_reciprical) for g in grads]
  - def _indexed_slices(self, grad, loss_scale_reciprical):
  + def _scale_grad(self, grad, loss_scale_reciprical):
  if isinstance(grad, ops.IndexedSlices):
  grad_vals = grad.values * loss_scale_reciprical
  return ops.IndexedSlices(grad_vals, grad.indices, grad.dense_shape)
  @@ -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._apply_gradients_cross_replica,
  args=(grads_and_vars, global_step, name))
  def _distributed_apply(self,
  @@ -197,10 +197,10 @@ 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,
  + self._apply_gradients_cross_replica(distribution, grads_and_vars,
  global_step, name)
  - def _maybe_apply_gradients_cross_replica(self, distribution, grads_and_vars,
  + def _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()
  