code: def evaluate():
      """Identify the best accuracy reached during training."""
      step = optimizer.iterations.numpy()
      if "best_accuracy" not in evaluate.__dict__:
          evaluate.best_accuracy = 0
      if step % FLAGS.ev_every != 0:
          return evaluate.best_accuracy
      aggregator = tf.keras.metrics.SparseCategoricalAccuracy()
      for example in ds_test:
          points, labels = example["points"], example["label"]
          logits = model(points, training=False)
          aggregator.update_state(labels, logits)
      accuracy = aggregator.result()
      evaluate.best_accuracy = max(accuracy, evaluate.best_accuracy)
      tf.summary.scalar(name="accuracy_test", data=accuracy, step=step)
      return evaluate.best_accuracy
  
file_path: graphics/tensorflow_graphics/projects/pointnet/train.py
imports: import tensorflow
  from tensorflow_graphics.datasets import modelnet40
  from tensorflow_graphics.nn.layer import pointnet
  import tqdm
  from .None import augment
  from .None import helpers
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate() and returns: evaluate, evaluate