code: def _load_celeba(data_path, postfix):
      """Load the CelebA dataset."""
      with tf.gfile.Open(
          os.path.join(data_path, "train" + postfix), "rb"
      ) as f:
          train_data = np.load(f)
      with tf.gfile.Open(
          os.path.join(data_path, "eval" + postfix), "rb"
      ) as f:
          eval_data = np.load(f)
      with tf.gfile.Open(
          os.path.join(data_path, "test" + postfix), "rb"
      ) as f:
          test_data = np.load(f)
      with tf.gfile.Open(
          os.path.join(data_path, "attr_train.npy"), "rb"
      ) as f:
          attr_train = np.load(f)
      with tf.gfile.Open(
          os.path.join(data_path, "attr_eval.npy"), "rb"
      ) as f:
          attr_eval = np.load(f)
      with tf.gfile.Open(
          os.path.join(data_path, "attr_test.npy"), "rb"
      ) as f:
          attr_test = np.load(f)
      attr_mask = [4, 8, 9, 11, 15, 20, 24, 31, 35, 39]
      attribute_names = [
          "Bald",
          "Black_Hair",
          "Blond_Hair",
          "Brown_Hair",
          "Eyeglasses",
          "Male",
          "No_Beard",
          "Smiling",
          "Wearing_Hat",
          "Young",
      ]
      attr_train = attr_train[:, attr_mask]
      attr_eval = attr_eval[:, attr_mask]
      attr_test = attr_test[:, attr_mask]
      return (
          train_data,
          eval_data,
          test_data,
          attr_train,
          attr_eval,
          attr_test,
          attribute_names,
      )
  
imports: import functools
  import importlib
  import os
  from magenta.models.latent_transfer import local_mnist
  import numpy
  from PIL import Image
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: _load_celeba(data_path, postfix) and returns: train_data,  eval_data,  test_data,  attr_train,  attr_eval,  attr_test,  attribute_names