code: def test_call(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_features = feature_extractor(
          np_speech_inputs, padding="max_length", return_tensors="np"
      ).input_features
      self.assertTrue(input_features.ndim == 3)
      self.assertTrue(
          input_features.shape[-1] == feature_extractor.nb_max_frames
      )
      self.assertTrue(
          input_features.shape[-2] == feature_extractor.feature_size
      )
  
      # Test not batched input
      encoded_sequences_1 = feature_extractor(
          speech_inputs[0], return_tensors="np"
      ).input_features
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs[0], return_tensors="np"
      ).input_features
      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_features
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs, return_tensors="np"
      ).input_features
      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 truncation required
      speech_inputs = [
          floats_list((1, x))[0]
          for x in range(200, (feature_extractor.n_samples + 500), 200)
      ]
      np_speech_inputs = [
          np.asarray(speech_input) for speech_input in speech_inputs
      ]
  
      speech_inputs_truncated = [
          x[: feature_extractor.n_samples] for x in speech_inputs
      ]
      np_speech_inputs_truncated = [
          np.asarray(speech_input)
          for speech_input in speech_inputs_truncated
      ]
  
      encoded_sequences_1 = feature_extractor(
          np_speech_inputs, return_tensors="np"
      ).input_features
      encoded_sequences_2 = feature_extractor(
          np_speech_inputs_truncated, return_tensors="np"
      ).input_features
      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 os
  import random
  import tempfile
  import unittest
  import numpy
  from transformers import is_speech_available
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
  from transformers.utils.import_utils import is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 4 for calling the WhisperFeatureExtractionTest obj's underlying member method code with example usage: obj.test_call() without return types