code: def test_audio_dataset_from_directory_multiclass(self):
      directory = self._prepare_directory(num_classes=4, count=15)
  
      dataset = audio_dataset_utils.audio_dataset_from_directory(
          directory,
          batch_size=8,
          output_sequence_length=30,
          label_mode=None,
      )
      batch = next(iter(dataset))
      self.assertEqual(batch.shape, (8, 30, 1))
  
      dataset = audio_dataset_utils.audio_dataset_from_directory(
          directory,
          batch_size=8,
          output_sequence_length=30,
          label_mode=None,
      )
      sample_count = 0
      iterator = iter(dataset)
      for batch in dataset:
          sample_count += next(iterator).shape[0]
      self.assertEqual(sample_count, 15)
  
      dataset = audio_dataset_utils.audio_dataset_from_directory(
          directory,
          batch_size=8,
          output_sequence_length=30,
          label_mode="int",
      )
      batch = next(iter(dataset))
      self.assertLen(batch, 2)
      self.assertEqual(batch[0].shape, (8, 30, 1))
      self.assertEqual(batch[0].dtype.name, "float32")
      self.assertEqual(batch[1].shape, (8,))
      self.assertEqual(batch[1].dtype.name, "int32")
  
      dataset = audio_dataset_utils.audio_dataset_from_directory(
          directory,
          batch_size=8,
          output_sequence_length=30,
          label_mode="categorical",
      )
      batch = next(iter(dataset))
      self.assertLen(batch, 2)
      self.assertEqual(batch[0].shape, (8, 30, 1))
      self.assertEqual(batch[0].dtype.name, "float32")
      self.assertEqual(batch[1].shape, (8, 4))
      self.assertEqual(batch[1].dtype.name, "float32")
  
imports: import os
  import numpy
  from keras import testing
  from keras.utils import audio_dataset_utils
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 7 for calling the AudioDatasetFromDirectoryTest obj's underlying member method code with example usage: obj.test_audio_dataset_from_directory_multiclass() without return types