code: def test_call_target(self):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      # create three inputs of length 800, 1000, and 1200
      speech_inputs = [
          floats_list((1, x))[0] for x in range(800, 1400, 200)
      ]
      np_speech_inputs = [
          np.asarray(speech_input) for speech_input in speech_inputs
      ]
  
      # Test feature size
      input_values = feature_extractor(
          audio_target=np_speech_inputs,
          padding=True,
          return_tensors="np",
      ).input_values
      self.assertTrue(input_values.ndim == 3)
      self.assertTrue(
          input_values.shape[-1] == feature_extractor.num_mel_bins
      )
  
      # Test not batched input
      encoded_sequences_1 = feature_extractor(
          speech_inputs[0], return_tensors="np"
      ).input_values
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs[0], return_tensors="np"
      ).input_values
      self.assertTrue(
          np.allclose(
              encoded_sequences_1, encoded_sequences_2, atol=1e-3
          )
      )
  
      # Test batched
      encoded_sequences_1 = feature_extractor(
          speech_inputs, return_tensors="np"
      ).input_values
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs, return_tensors="np"
      ).input_values
      for enc_seq_1, enc_seq_2 in zip(
          encoded_sequences_1, encoded_sequences_2
      ):
          self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
  
      # Test 2-D numpy arrays are batched.
      speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
      np_speech_inputs = np.asarray(speech_inputs)
      encoded_sequences_1 = feature_extractor(
          speech_inputs, return_tensors="np"
      ).input_values
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs, return_tensors="np"
      ).input_values
      for enc_seq_1, enc_seq_2 in zip(
          encoded_sequences_1, encoded_sequences_2
      ):
          self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
  
imports: import itertools
  import random
  import unittest
  import numpy
  from transformers import BatchFeature, SpeechT5FeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 9 for calling the SpeechT5FeatureExtractionTest obj's underlying member method code with example usage: obj.test_call_target() without return types