code: def load_data():
      """Load data.
  
      Load GLUE/MRPC dataset, and convert the dictionary format to
      (features, label), where `features` is a tuple of all input sentences.
      """
      feature_names = ("sentence1", "sentence2")
  
      def split_features(x):
          # GLUE comes with dictonary data, we convert it to a uniform format
          # (features, label), where features is a tuple consisting of all
          # features. This format is necessary for using KerasNLP preprocessors.
          features = tuple([x[name] for name in feature_names])
          label = x["label"]
          return (features, label)
  
      train_ds, test_ds, validation_ds = tfds.load(
          "glue/mrpc",
          split=["train", "test", "validation"],
      )
  
      train_ds = (
          train_ds.map(
              split_features, num_parallel_calls=tf.data.AUTOTUNE
          )
          .batch(FLAGS.batch_size)
          .prefetch(tf.data.AUTOTUNE)
      )
      test_ds = (
          test_ds.map(
              split_features, num_parallel_calls=tf.data.AUTOTUNE
          )
          .batch(FLAGS.batch_size)
          .prefetch(tf.data.AUTOTUNE)
      )
      validation_ds = (
          validation_ds.map(
              split_features, num_parallel_calls=tf.data.AUTOTUNE
          )
          .batch(FLAGS.batch_size)
          .prefetch(tf.data.AUTOTUNE)
      )
      return train_ds, test_ds, validation_ds
  
file_path: keras/benchmarks/model_benchmark/bert_benchmark.py
imports: import time
  import keras_nlp
  import numpy
  import tensorflow
  import tensorflow_datasets
  from absl import app
  from absl import flags
  from absl import logging
  from model_benchmark.benchmark_utils import BenchmarkMetricsCallback
  import keras
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: train_ds,  test_ds,  validation_ds, features,  label