code: def test_feedable(self):
      handle = tf.placeholder(tf.string, shape=[])
      iter_ = tf.data.Iterator.from_string_handle(
          handle,
          tf.compat.v1.data.get_output_types(self.dataset),
          tf.compat.v1.data.get_output_shapes(self.dataset),
      )
      feed_iterator = tf.compat.v1.data.make_initializable_iterator(
          self.dataset
      )
      feed_handle = self.sess.run(feed_iterator.string_handle())
      data_gen = TensorFlowDataGenerator(
          sess=self.sess,
          iterator=iter_,
          iterator_type="feedable",
          iterator_arg=(feed_iterator, {handle: feed_handle}),
          size=10,
          batch_size=5,
      )
      x, y = data_gen.get_batch()
  
      # Check return types
      self.assertTrue(isinstance(x, np.ndarray))
      self.assertTrue(isinstance(y, np.ndarray))
  
      # Check shapes
      self.assertEqual(x.shape, (5, 5, 5, 1))
      self.assertEqual(y.shape, (5, 10))
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import tensorflow
  import numpy
  from keras.preprocessing.image import ImageDataGenerator
  from art.data_generators import KerasDataGenerator, PyTorchDataGenerator, MXDataGenerator, TensorFlowDataGenerator
  from art.data_generators import TensorFlowV2DataGenerator, NumpyDataGenerator
  from tests.utils import master_seed
label_desc: Use image node_id 5 for calling the TestTensorFlowDataGenerator obj's underlying member method code with example usage: obj.test_feedable() without return types