class_docstr: A class for querying and converting data.
docstr: Returns a batch of data and one-hot labels.
code: def provide_data(self, batch_size):
      """Returns a batch of data and one-hot labels."""
      with tf.name_scope("inputs"):
          with tf.device("/cpu:0"):
              dataset = self.dataset.provide_dataset()
              dataset = dataset.shuffle(buffer_size=1000)
              dataset = dataset.map(self._map_fn, num_parallel_calls=4)
              dataset = dataset.batch(batch_size)
              dataset = dataset.prefetch(1)
  
              iterator = tf.data.make_initializable_iterator(dataset)
              tf.add_to_collection(
                  tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer
              )
  
              data, one_hot_labels = iterator.get_next()
              data.set_shape([batch_size, None, None, None])
              one_hot_labels.set_shape([batch_size, None])
              return data, one_hot_labels
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from magenta.models.gansynth.lib import datasets
  from magenta.models.gansynth.lib import train_util
  from magenta.models.gansynth.lib.specgrams_helper import SpecgramsHelper
  import tensorflow.compat.v1
label_desc: Use image node_id 8 for calling the DataHelper obj's underlying member method code with example usage: obj.provide_data(batch_size) and returns: data,  one_hot_labels