code: def test_prediction_head(self):
      model = PatchTSTForPrediction.from_pretrained(
          "namctin/patchtst_etth1_forecast"
      ).to(torch_device)
      batch = prepare_batch(file="test-batch.pt")
  
      torch.manual_seed(0)
      with torch.no_grad():
          output = model(
              past_values=batch["past_values"].to(torch_device),
              future_values=batch["future_values"].to(torch_device),
          ).prediction_outputs
      expected_shape = torch.Size(
          [
              64,
              model.config.prediction_length,
              model.config.num_input_channels,
          ]
      )
      self.assertEqual(output.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [[0.5142, 0.6928, 0.6118, 0.5724, -0.3735, -0.1336, -0.7124]],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              output[0, :1, :7], expected_slice, atol=TOLERANCE
          )
      )
  
imports: import inspect
  import random
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import is_torch_available
  from transformers.models.auto import get_values
  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 PatchTSTModelIntegrationTests obj's underlying member method code with example usage: obj.test_prediction_head() without return types