code: def evaluate(model, features, labels, mask):
      logits = model(features, training=False)
      logits = logits[mask]
      labels = labels[mask]
      indices = tf.math.argmax(logits, axis=1)
      acc = tf.reduce_mean(tf.cast(indices == labels, dtype=tf.float32))
      return acc.numpy().item()
  
file_path: dgl/examples/tensorflow/gcn/gcn_builtin.py
imports: import argparse
  import math
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import tensorflow
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset, register_data_args
  from tensorflow.keras import layers
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, features, labels, mask) and returns: acc