code: def test_annotate_with_predictions(backend_config, setup, label_type):
      dataset, anno_key, label_field, labels = setup
      label_field += f"-{label_type}"
  
      # add dummy labels
      for smp in dataset:
          smp[label_field] = _generate_dummy_labels(label_type, labels)
          smp.save()
  
      dataset.annotate(
          anno_key,
          label_field=label_field,
          project_name=f"labelstudio_{label_type}_tests",
          label_type=label_type,
          backend="labelstudio",
          **backend_config,
      )
  
      # test that project tasks have predictions
      res = dataset.load_annotation_results(anno_key, **backend_config)
      ls_project = _get_project(res)
      assert len(ls_project.tasks_ids) == len(dataset)
      assert ls_project.get_predictions_coverage()["undefined"] == 1.0
  
      # test that annotations can be loaded back in lieu of existing labels
      ls_project.create_annotations_from_predictions()
      dataset.load_annotations(anno_key, **backend_config)
  
imports: import os
  import random
  import numpy
  import pytest
  import fiftyone
  import fiftyone.zoo
  import fiftyone.utils.labelstudio
  import fiftyone.core.labels
label_desc: Use image node_id 11 for calling a global function with example usage: test_annotate_with_predictions(backend_config, setup, label_type) without return types