code: def test_snowman_image_captioning_batch(self):
      url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
  
      image = Image.open(requests.get(url, stream=True).raw)
      image.save("new_image.jpg")
      image = Image.open("new_image.jpg")
  
      model = AutoModelForVision2Seq.from_pretrained(
          "microsoft/kosmos-2-patch14-224"
      ).to(torch_device)
  
      prompt = [
          "<grounding>Describe this image in detail:",
          "<grounding>An image of",
      ]
  
      # left padding
      processor = AutoProcessor.from_pretrained(
          "microsoft/kosmos-2-patch14-224", padding_side="left"
      )
  
      (
          scores,
          generated_ids,
          generated_text,
          processed_text,
          final_text_with_entities,
      ) = self.run_example(
          prompt, [image] * len(prompt), model, processor
      )
      all_final_text = [x[0] for x in final_text_with_entities]
      all_entities = [x[1] for x in final_text_with_entities]
  
      # left padding gives identical results as non-padding
      EXPECTED_PROCESSED_TEXT_0 = (
          "<grounding> Describe this image in detail: The image features a snowman sitting by<phrase> a campfire"
          "</phrase><object><patch_index_0005><patch_index_1007></object> in the snow. He is wearing<phrase> a hat"
          "</phrase><object><patch_index_0048><patch_index_0250></object>,<phrase> scarf</phrase><object>"
          "<patch_index_0240><patch_index_0604></object>, and<phrase> gloves</phrase><object><patch_index_0400>"
          "<patch_index_0532></object>, with<phrase> a pot</phrase><object><patch_index_0610><patch_index_0872>"
          "</object> nearby and<phrase> a cup</phrase><object><patch_index_0796><patch_index_1023></object> placed "
          "nearby. The snowman appears to be enjoying the warmth of the fire, and it appears to have a warm and cozy "
          "atmosphere."
      )
      EXPECTED_PROCESSED_TEXT_1 = (
          "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> "
          "warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
      )
      self.assertListEqual(
          processed_text,
          [EXPECTED_PROCESSED_TEXT_0, EXPECTED_PROCESSED_TEXT_1],
      )
  
      EXPECTED_FINAL_TEXT_0 = (
          "Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is "
          "wearing a hat, scarf, and gloves, with a pot nearby and a cup placed nearby. The snowman appears to be "
          "enjoying the warmth of the fire, and it appears to have a warm and cozy atmosphere."
      )
      EXPECTED_FINAL_TEXT_1 = (
          "An image of a snowman warming himself by a fire."
      )
      self.assertListEqual(
          all_final_text, [EXPECTED_FINAL_TEXT_0, EXPECTED_FINAL_TEXT_1]
      )
  
      EXPECTED_ENTITIES_0 = [
          (
              "a campfire",
              (71, 81),
              [(0.171875, 0.015625, 0.484375, 0.984375)],
          ),
          (
              "a hat",
              (109, 114),
              [(0.515625, 0.046875, 0.828125, 0.234375)],
          ),
          (
              "scarf",
              (116, 121),
              [(0.515625, 0.234375, 0.890625, 0.578125)],
          ),
          (
              "gloves",
              (127, 133),
              [(0.515625, 0.390625, 0.640625, 0.515625)],
          ),
          (
              "a pot",
              (140, 145),
              [(0.078125, 0.609375, 0.265625, 0.859375)],
          ),
          (
              "a cup",
              (157, 162),
              [(0.890625, 0.765625, 0.984375, 0.984375)],
          ),
      ]
      EXPECTED_ENTITIES_1 = [
          (
              "a snowman",
              (12, 21),
              [(0.390625, 0.046875, 0.984375, 0.828125)],
          ),
          (
              "a fire",
              (41, 47),
              [(0.171875, 0.015625, 0.484375, 0.890625)],
          ),
      ]
      self.assertListEqual(
          all_entities, [EXPECTED_ENTITIES_0, EXPECTED_ENTITIES_1]
      )
  
      # right padding
      processor = AutoProcessor.from_pretrained(
          "microsoft/kosmos-2-patch14-224"
      )
  
      (
          scores,
          generated_ids,
          generated_text,
          processed_text,
          final_text_with_entities,
      ) = self.run_example(
          prompt, [image] * len(prompt), model, processor
      )
      all_final_text = [x[0] for x in final_text_with_entities]
      all_entities = [x[1] for x in final_text_with_entities]
  
      # For right padding, only the non-padded sequences will give the same results as non-padding
      self.assertEqual(processed_text[0], EXPECTED_PROCESSED_TEXT_0)
      self.assertEqual(all_final_text[0], EXPECTED_FINAL_TEXT_0)
      self.assertListEqual(all_entities[0], EXPECTED_ENTITIES_0)
  
imports: import copy
  import inspect
  import os
  import tempfile
  import unittest
  import numpy
  import requests
  from transformers import AutoModelForVision2Seq, AutoProcessor, Kosmos2Config
  from transformers.models.kosmos2.configuration_kosmos2 import Kosmos2TextConfig, Kosmos2VisionConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the Kosmos2ModelIntegrationTest obj's underlying member method code with example usage: obj.test_snowman_image_captioning_batch() without return types