code: def test_mask_time_prob_ctc(self):
      model = UniSpeechForCTC.from_pretrained(
          "hf-internal-testing/tiny-random-unispeech",
          mask_time_prob=0.2,
          mask_time_length=2,
      )
      model.to(torch_device).train()
      processor = Wav2Vec2Processor.from_pretrained(
          "hf-internal-testing/tiny-random-unispeech",
          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
  import pytest
  from datasets import load_dataset
  from transformers import UniSpeechConfig, is_torch_available
  from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 18 for calling the UniSpeechRobustModelTest obj's underlying member method code with example usage: obj.test_mask_time_prob_ctc() without return types