code: def test_inference_image_classification_head_with_teacher(self):
      model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
          "snap-research/efficientformer-l1-300"
      ).to(
          torch_device
      )
  
      image_processor = self.default_image_processor
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = (1, 1000)
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor([-0.1312, 0.4353, -1.0499]).to(
          torch_device
      )
      self.assertTrue(
          torch.allclose(
              outputs.logits[0][:3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  import warnings
  from typing import List
  from transformers import EfficientFormerConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  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 3 for calling the EfficientFormerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head_with_teacher() without return types