code: def test_inference_ctc_batched(self):
      model = Data2VecAudioForCTC.from_pretrained(
          "facebook/data2vec-audio-base-960h"
      ).to(torch_device)
      processor = Wav2Vec2Processor.from_pretrained(
          "hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True
      )
  
      input_speech = self._load_datasamples(4)
  
      inputs = processor(
          input_speech, return_tensors="pt", padding=True
      )
  
      input_values = inputs.input_values.to(torch_device)
  
      with torch.no_grad():
          logits = model(input_values).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      predicted_trans = processor.batch_decode(predicted_ids)
  
      EXPECTED_TRANSCRIPTIONS = [
          "a man said to the universe sir i exist",
          "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
          "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
          " him with thousands of spectators were trivialities not worth thinking about",
          "his instant of panic was followed by a small sharp blow high on his chest",
      ]
      self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
  
imports: import math
  import unittest
  import numpy
  from datasets import load_dataset
  from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
  from transformers import Data2VecAudioConfig, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init
label_desc: Use image node_id 4 for calling the Data2VecAudioModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_ctc_batched() without return types