code: def format_predictions(
      *,
      scores: np.ndarray,
      labels: np.ndarray,
      boxes: np.ndarray,
      image_sizes: np.ndarray,
      image_ids: np.ndarray,
      label_shift: int = 0,
  ) -> List[Dict[str, Any]]:
      """Formats predictions to COCO annotation format.
  
      Args:
        scores: [num_images, num_instances] array of confidence scores.
        labels: [num_images, num_instances] array of label ids.
        boxes: [num_images, num_instances, 4] array of bounding boxes in relative
          COCO format (cx, cy, w, h).
        image_sizes: [num_images, 2] array of original unpadded image height and
          width in pixels.
        image_ids: COCO/LVIS image IDs.
        label_shift: Value that will be added to the model output labels in the
          prediction JSON files. The model predictions are zero-indexed. COCO or
          LVIS use one-indexed labels, so label_shift should be 1 for these
          datasets. Set it to 0 for zero-indexed datasets.
  
      Returns:
        List of dicts that can be saved as COCO/LVIS prediction JSON for evaluation.
      """
      predictions = []
      num_batches, num_instances = scores.shape
      for batch in range(num_batches):
          h, w = image_sizes[batch]
          for instance in range(num_instances):
              label = int(labels[batch, instance])
              if not label:
                  continue
              score = float(scores[batch, instance])
              # Internally, we use center coordinates, but COCO uses corner coordinates:
              bcx, bcy, bw, bh = unpad_box(
                  boxes[batch, instance], image_w=w, image_h=h
              )
              bx = bcx - bw / 2
              by = bcy - bh / 2
              predictions.append(
                  {
                      "image_id": int(image_ids[batch]),
                      "category_id": label + label_shift,
                      "bbox": [
                          float(bx * w),
                          float(by * h),
                          float(bw * w),
                          float(bh * h),
                      ],
                      "score": score,
                  }
              )
      return predictions
  
imports: import collections
  import datetime
  import functools
  import json
  import multiprocessing
  import os
  import re
  import tempfile
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
  import urllib
  import zipfile
  from absl import app
  from absl import flags
  from absl import logging
  from clu import preprocess_spec
  from flax import linen
  import jax
  from jax.experimental.compilation_cache import compilation_cache
  import jax.numpy
  from lvis.eval import LVISEval
  from lvis.lvis import LVIS
  from lvis.results import LVISResults
  from matplotlib import pyplot
  import ml_collections
  import numpy
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from scenic.projects.owl_vit import configs
  from scenic.projects.owl_vit import models
  from scenic.projects.owl_vit.preprocessing import image_ops
  from scenic.projects.owl_vit.preprocessing import label_ops
  from scenic.projects.owl_vit.preprocessing import modalities
  import tensorflow
  import tensorflow_datasets
  import tqdm
label_desc: Use image node_id 8 for calling a global function with example usage: format_predictions() and returns: predictions