code: def plot_image(pixels, image_id, gt_by_image, pred_by_image, labels):
      """Plots an image with annotations."""
      fig, axs = plt.subplots(1, 2, figsize=(12, 6))
  
      # Plot ground-truth:
      ax = axs[0]
      ax.imshow(pixels)
      for ann in gt_by_image[image_id]:
          plot_box(ax, ann, color="g", labels=labels)
      ax.set_title(f"Ground truth (Image ID: {image_id})")
  
      # Plot prediction:
      ax = axs[1]
      ax.imshow(pixels)
      anns = pred_by_image[image_id]
      if anns:
          n = (
              _MIN_BOXES_TO_PLOT
              + len(gt_by_image[image_id]) * _PRED_BOX_PLOT_FACTOR
          )
          n = min(n, len(anns))
          threshold = np.partition(
              np.array([a["score"] for a in anns]), -n
          )[-n]
          threshold = max(threshold, _PLOTTING_SCORE_THRESHOLD)
          for ann in gt_by_image[image_id]:
              plot_box(ax, ann, color="g", label=False)
          for ann in anns:
              if ann["score"] <= threshold:
                  continue
              plot_box(
                  ax, ann, color="r", labels=labels, score=ann["score"]
              )
      ax.set_title("Predictions")
  
      fig.tight_layout()
      return fig
  
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 17 for calling a global function with example usage: plot_image(pixels, image_id, gt_by_image, pred_by_image, labels) and returns: fig