code: def test_inference_head(self):
      model = AutoformerForPrediction.from_pretrained(
          "huggingface/autoformer-tourism-monthly"
      ).to(torch_device)
      batch = prepare_batch("val-batch.pt")
      with torch.no_grad():
          output = model(
              past_values=batch["past_values"],
              past_time_features=batch["past_time_features"],
              past_observed_mask=batch["past_observed_mask"],
              static_categorical_features=batch[
                  "static_categorical_features"
              ],
          ).encoder_last_hidden_state
      expected_shape = torch.Size(
          (64, model.config.context_length, model.config.d_model)
      )
      self.assertEqual(output.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-0.0734, -0.9036, 0.8358],
              [4.7186, 2.4113, 1.9581],
              [1.7953, 2.3558, 1.2970],
          ],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              output[0, :3, :3], expected_slice, atol=TOLERANCE
          )
      )
  
imports: import inspect
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import is_torch_available
  from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the AutoformerModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_head() without return types