code: def test_inference_mask_generation_batched_image_one_point(self):
      model = TFSamModel.from_pretrained("facebook/sam-vit-base")
      processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
  
      raw_image = prepare_image()
      raw_dog_image = prepare_dog_img()
  
      input_points = [[[820, 1080]], [[220, 470]]]
  
      inputs = processor(
          images=[raw_image, raw_dog_image],
          input_points=input_points,
          return_tensors="tf",
      )
  
      outputs = model(**inputs)
      scores_batched = tf.squeeze(outputs.iou_scores)
  
      input_points = [[[220, 470]]]
  
      inputs = processor(
          images=raw_dog_image,
          input_points=input_points,
          return_tensors="tf",
      )
  
      outputs = model(**inputs)
      scores_single = tf.squeeze(outputs.iou_scores)
      self.assertTrue(
          np.allclose(
              scores_batched[1, :].numpy(),
              scores_single.numpy(),
              atol=1e-4,
          )
      )
  
imports: from __future__ import annotations
  import inspect
  import unittest
  import numpy
  import requests
  from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig
  from transformers.testing_utils import require_tf, slow
  from transformers.utils import is_tf_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 9 for calling the TFSamModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_mask_generation_batched_image_one_point() without return types