@tf.function
def _evaluate_steps(self, per_replica_features, per_replica_labels):
    '\n        One step evaluation across replica.\n        Args:\n          per_replica_features: the batched features.\n          per_replica_labels: the batched labels.\n        Returns:\n          The loss corresponding to the given batch.\n        '
    (per_replica_loss, per_replica_logits) = self.args.strategy.experimental_run_v2(self._run_model, args=(per_replica_features, per_replica_labels, False))
    try:
        reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0)
    except ValueError:
        reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None)
    return (reduced_loss, per_replica_logits)
One step evaluation across replica.
Args:
  per_replica_features: the batched features.
  per_replica_labels: the batched labels.
Returns:
  The loss corresponding to the given batch.
@tf.function
def _evaluate_steps(self, per_replica_features, per_replica_labels):
    '\n        One step evaluation across replica.\n        Args:\n          per_replica_features: the batched features.\n          per_replica_labels: the batched labels.\n        Returns:\n          The loss corresponding to the given batch.\n        '
    (per_replica_loss, per_replica_logits) = self.args.strategy.experimental_run_v2(self._run_model, args=(per_replica_features, per_replica_labels, False))
    try:
        reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0)
    except ValueError:
        reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None)
    return (reduced_loss, per_replica_logits)