code: def test_model_phi_1_5_logits(self):
      input_ids = {
          "input_ids": torch.tensor(
              [[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]],
              dtype=torch.long,
              device=torch_device,
          )
      }
  
      model = PhiForCausalLM.from_pretrained("microsoft/phi-1_5").to(
          torch_device
      )
      model.eval()
  
      output = model(**input_ids).logits
  
      EXPECTED_OUTPUT = torch.tensor([[12.2922, 13.3507,  8.6963,  9.1355,  9.3502,  9.2667, 14.2027, 13.1363, 13.5446, 11.1337,  9.9279, 16.7195, 13.0768, 14.9141, 11.9965,  8.0233, 10.3129, 10.6118, 10.0204,  9.3827,  8.8344,  8.2806,  8.0153,  8.0540, 7.0964, 16.5743, 11.1256,  9.6987, 11.4770, 10.5440], [12.3323, 14.6050,  8.9986,  8.1580,  9.5654,  6.6728, 12.5966, 12.6662, 12.2784, 11.7522,  8.2039, 16.3102, 11.2203, 13.6088, 12.0125,  9.1021, 9.8216, 10.0987,  9.0926,  8.4260,  8.8009,  7.6547,  6.8075,  7.7881, 7.4501, 15.7451, 10.5053,  8.3129, 10.0027,  9.2612]]).to(torch_device)  # fmt: skip
  
      self.assertTrue(
          torch.allclose(
              EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4
          )
      )
  
imports: import unittest
  import pytest
  from transformers import PhiConfig, is_torch_available
  from transformers.testing_utils import require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  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 2 for calling the PhiIntegrationTest obj's underlying member method code with example usage: obj.test_model_phi_1_5_logits() without return types