code: def test_inference_image_classification_head(self):
      model = TFRegNetForImageClassification.from_pretrained(
          TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]
      )
  
      feature_extractor = self.default_feature_extractor
      image = prepare_img()
      inputs = feature_extractor(images=image, return_tensors="tf")
  
      # forward pass
      outputs = model(**inputs, training=False)
  
      # verify the logits
      expected_shape = tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = tf.constant([-0.4180, -1.5051, -3.4836])
  
      tf.debugging.assert_near(
          outputs.logits[0, :3], expected_slice, atol=1e-4
      )
  
imports: import inspect
  import unittest
  from typing import List, Tuple
  from transformers import RegNetConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 2 for calling the RegNetModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types