code: def test_inference_coco_en(self):
      loc = "ydshieh/vit-gpt2-coco-en"
  
      feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
      tokenizer = AutoTokenizer.from_pretrained(loc)
      model = TFVisionEncoderDecoderModel.from_pretrained(loc)
  
      # We will verify our results on an image of cute cats
      img = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      pixel_values = feature_extractor(
          images=img, return_tensors="tf"
      ).pixel_values
  
      decoder_input_ids = tf.constant(
          [[model.config.decoder_start_token_id]]
      )
  
      logits = model(pixel_values, decoder_input_ids)[0].numpy()
  
      # verify the logits
      expected_shape = (1, 1, model.config.decoder.vocab_size)
      self.assertEqual(logits.shape, expected_shape)
  
      EXPECTED_LOGIT_SLICE = np.array(
          [
              -38.705807,
              -30.639929,
              -31.41903,
              -39.012012,
              -38.38696,
              -34.887207,
              -33.290855,
              -35.68447,
              -38.508484,
              -36.124645,
          ]
      )
      max_diff = np.amax(
          np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE)
      )
      self.assertLessEqual(max_diff, 1e-4)
  
      def generate_step(pixel_values):
          outputs = model.generate(
              pixel_values,
              max_length=16,
              num_beams=4,
              return_dict_in_generate=True,
              output_scores=True,
          )
          output_ids = outputs.sequences
          preds = tokenizer.batch_decode(
              output_ids, skip_special_tokens=True
          )
          preds = [pred.strip() for pred in preds]
  
          return preds, outputs.scores.numpy()
  
      preds, scores = generate_step(pixel_values)
  
      # should produce
      # ["a cat laying on top of a couch next to another cat"]
      self.assertEqual(
          preds, ["a cat laying on top of a couch next to another cat"]
      )
  
imports: import copy
  import os
  import tempfile
  import unittest
  import numpy
  from transformers import is_tf_available, is_torch_available, is_vision_available
  from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_torch, require_vision, slow, torch_device
  from transformers.utils.generic import ModelOutput
  from ...test_modeling_tf_common import floats_tensor, ids_tensor
  from ..gpt2.test_modeling_tf_gpt2 import TFGPT2ModelTester
  from ..vit.test_modeling_tf_vit import TFViTModelTester
label_desc: Use image node_id 1 for calling the TFViT2GPT2ModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_coco_en() and returns: preds,  outputs