code: def test_inference_pretrained_batched(self):
      model = SEWModel.from_pretrained("asapp/sew-tiny-100k").to(
          torch_device
      )
      processor = Wav2Vec2FeatureExtractor.from_pretrained(
          "asapp/sew-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 implementation
      expected_outputs_first = torch.tensor(
          [
              [
                  [0.1509, 0.5372, 0.3061, -0.1694],
                  [-0.1700, 0.5764, 0.2753, -0.1299],
                  [0.1281, 0.7949, 0.2342, -0.1624],
                  [-0.1627, 0.6710, 0.2215, -0.1317],
              ],
              [
                  [0.0408, 1.4355, 0.8605, -0.0968],
                  [0.0393, 1.2368, 0.6826, 0.0364],
                  [-0.1269, 1.9215, 1.1677, -0.1297],
                  [-0.1654, 1.6524, 0.6877, -0.0196],
              ],
          ],
          device=torch_device,
      )
      expected_outputs_last = torch.tensor(
          [
              [
                  [1.3379, -0.1450, -0.1500, -0.0515],
                  [0.8364, -0.1680, -0.1248, -0.0689],
                  [1.2791, -0.1507, -0.1523, -0.0564],
                  [0.8208, -0.1690, -0.1199, -0.0751],
              ],
              [
                  [0.6959, -0.0861, -0.1235, -0.0861],
                  [0.4700, -0.1686, -0.1141, -0.1199],
                  [1.0776, -0.1137, -0.0124, -0.0472],
                  [0.5774, -0.1675, -0.0376, -0.0823],
              ],
          ],
          device=torch_device,
      )
      expected_output_sum = 62146.7422
  
      self.assertTrue(
          torch.allclose(
              outputs[:, :4, :4], expected_outputs_first, atol=5e-3
          )
      )
      self.assertTrue(
          torch.allclose(
              outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3
          )
      )
      self.assertTrue(abs(outputs.sum() - expected_output_sum) < 5)
  
imports: import math
  import unittest
  import pytest
  from transformers import SEWConfig, 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 SEWModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_pretrained_batched() without return types