code: def test_call(self):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      tokenizer = self.get_tokenizer()
      # 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 not batched input
      encoded_sequences_1 = tokenizer(
          speech_inputs[0], return_tensors="np"
      ).input_values
      encoded_sequences_2 = tokenizer(
          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 = tokenizer(
          speech_inputs, return_tensors="np"
      ).input_values
      encoded_sequences_2 = tokenizer(
          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 inspect
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import numpy
  from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer
  from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
  from transformers.testing_utils import require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 6 for calling the Wav2Vec2TokenizerTest obj's underlying member method code with example usage: obj.test_call() without return types