code: def test_inference_classification_head(self):
      model = SqueezeBertForSequenceClassification.from_pretrained(
          "squeezebert/squeezebert-mnli"
      )
  
      input_ids = torch.tensor(
          [[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]
      )
      output = model(input_ids)[0]
      expected_shape = torch.Size((1, 3))
      self.assertEqual(output.shape, expected_shape)
      expected_tensor = torch.tensor([[0.6401, -0.0349, -0.6041]])
      self.assertTrue(
          torch.allclose(output, expected_tensor, atol=1e-4)
      )
  
imports: import unittest
  from transformers import SqueezeBertConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the SqueezeBertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_classification_head() without return types