code: def test_inference_image_classification_head_imagenet_1k(self):
      model = TFData2VecVisionForImageClassification.from_pretrained(
          "facebook/data2vec-vision-base-ft1k"
      )
  
      feature_extractor = self.default_feature_extractor
      image = prepare_img()
      inputs = feature_extractor(images=image, return_tensors="tf")
  
      # forward pass
      outputs = model(**inputs)
      logits = outputs.logits
  
      # verify the logits
      expected_shape = tf.convert_to_tensor([1, 1000])
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = tf.convert_to_tensor([0.3277, -0.1395, 0.0911])
  
      tf.debugging.assert_near(logits[0, :3], expected_slice, atol=1e-4)
  
      expected_top2 = [
          model.config.label2id[i]
          for i in ["remote control, remote", "tabby, tabby cat"]
      ]
      self.assertEqual(
          tf.nn.top_k(outputs.logits[0], 2).indices.numpy().tolist(),
          expected_top2,
      )
  
imports: import collections.abc
  import inspect
  import unittest
  import numpy
  from transformers import Data2VecVisionConfig
  from transformers.file_utils import cached_property, is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, require_vision, slow
  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 TFData2VecVisionModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head_imagenet_1k() without return types