code: def test_mask_feature_prob_ctc(self):
      model = Data2VecAudioForCTC.from_pretrained(
          "hf-internal-testing/tiny-random-data2vec-seq-class",
          mask_feature_prob=0.2,
          mask_feature_length=2,
      )
      model.to(torch_device).train()
      processor = Wav2Vec2Processor.from_pretrained(
          "hf-internal-testing/tiny-random-wav2vec2",
          return_attention_mask=True,
      )
  
      batch_duration_in_seconds = [1, 3, 2, 6]
      input_features = [
          np.random.random(16_000 * s)
          for s in batch_duration_in_seconds
      ]
  
      batch = processor(
          input_features,
          padding=True,
          sampling_rate=processor.feature_extractor.sampling_rate,
          return_tensors="pt",
      )
  
      logits = model(
          input_values=batch["input_values"].to(torch_device),
          attention_mask=batch["attention_mask"].to(torch_device),
      ).logits
  
      self.assertEqual(logits.shape, (4, 1498, 32))
  
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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 21 for calling the Data2VecAudioModelTest obj's underlying member method code with example usage: obj.test_mask_feature_prob_ctc() without return types