code: def test_inference_pretrained_batched(self):
      model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k").to(
          torch_device
      )
      processor = Wav2Vec2FeatureExtractor.from_pretrained(
          "asapp/sew-d-tiny-100k"
      )
  
      input_speech = self._load_datasamples(2)
  
      inputs = processor(
          input_speech, return_tensors="pt", padding=True
      )
  
      input_values = inputs.input_values.to(torch_device)
  
      with torch.no_grad():
          outputs = model(input_values).last_hidden_state
  
      # expected outputs taken from the original SEW-D implementation
      expected_outputs_first = torch.tensor(
          [
              [
                  [-0.1619, 0.6995, 0.4062, -0.1014],
                  [-0.1364, 0.5960, 0.0952, -0.0873],
                  [-0.1572, 0.5718, 0.4228, -0.0864],
                  [-0.1325, 0.6823, 0.1387, -0.0871],
              ],
              [
                  [-0.1296, 0.4008, 0.4952, -0.1450],
                  [-0.1152, 0.3693, 0.3037, -0.1290],
                  [-0.1194, 0.6074, 0.3531, -0.1466],
                  [-0.1113, 0.3135, 0.2224, -0.1338],
              ],
          ],
          device=torch_device,
      )
      expected_outputs_last = torch.tensor(
          [
              [
                  [-0.1577, 0.5108, 0.8553, 0.2550],
                  [-0.1530, 0.3580, 0.6143, 0.2672],
                  [-0.1535, 0.4954, 0.8503, 0.1387],
                  [-0.1572, 0.3363, 0.6217, 0.1490],
              ],
              [
                  [-0.1338, 0.5459, 0.9607, -0.1133],
                  [-0.1502, 0.3738, 0.7313, -0.0986],
                  [-0.0953, 0.4708, 1.0821, -0.0944],
                  [-0.1474, 0.3598, 0.7248, -0.0748],
              ],
          ],
          device=torch_device,
      )
      expected_output_sum = 54201.0469
  
      self.assertTrue(
          torch.allclose(
              outputs[:, :4, :4], expected_outputs_first, atol=1e-3
          )
      )
      self.assertTrue(
          torch.allclose(
              outputs[:, -4:, -4:], expected_outputs_last, atol=1e-3
          )
      )
      self.assertTrue(abs(outputs.sum() - expected_output_sum) < 1)
  
imports: import math
  import unittest
  import pytest
  from transformers import SEWDConfig, 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 2 for calling the SEWDModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_pretrained_batched() without return types