code: def get_predictions(
      config: ml_collections.ConfigDict,
      checkpoint_path: Optional[str],
      tfds_name: str,
      split: str,
      top_k: int = 300,
      exclusive_classes: bool = False,
      label_shift: int = 0,
  ) -> List[Dict[str, Any]]:
      """Gets predictions from an OWL-ViT model for a whole TFDS dataset.
  
      These predictions can then be evaluated using the COCO/LVIS APIs.
  
      Args:
        config: Model config.
        checkpoint_path: Checkpoint path (overwrites the path in the model config).
        tfds_name: TFDS dataset to get predictions for.
        split: Dataset split to get predictions for.
        top_k: Number of predictions to retain per image.
        exclusive_classes: If True, the top class for each box is returned. If
          False, classes are considered to be non-exclusive (multi-label setting),
          and the top-k computations happens globally across all scores, not just
          the maximum logit for each output token.
        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:
        Dictionary of predictions.
      """
  
      # Load model and variables:
      module = models.TextZeroShotDetectionModule(
          body_configs=config.model.body,
          normalize=config.model.normalize,
          box_bias=config.model.box_bias,
      )
      module.tokenize("")  # Warm up the tokenizer.
      variables = module.load_variables(checkpoint_path=checkpoint_path)
      embed_queries = get_embed_queries_fn(module, variables)
      predict = get_predict_fn(module, variables)
      pmapped_top_k = jax.pmap(
          get_top_k, static_broadcasted_argnums=(2, 3)
      )
  
      # Create dataset:
      dataset, class_names = get_dataset(
          tfds_name=tfds_name,
          split=split,
          input_size=config.dataset_configs.input_size,
          tfds_data_dir=FLAGS.tfds_data_dir,
          tfds_download_dir=FLAGS.tfds_download_dir,
          data_format=FLAGS.data_format,
      )
  
      # Embed queries:
      query_embeddings = []
      for template in label_ops.CLIP_BEST_PROMPT_TEMPLATES:
          tokenized_queries = tokenize_queries(
              module.tokenize,
              class_names,
              template,
              max_token_len=config.dataset_configs.max_query_length,
          )
          query_embeddings.append(
              embed_queries(np.array(tokenized_queries))
          )  # pytype: disable=wrong-arg-types  # jax-ndarray
  
      # Prediction loop:
      predictions = []
      for batch in tqdm.tqdm(
          dataset.as_numpy_iterator(),
          desc="Inference progress",
          total=int(dataset.cardinality().numpy()),
      ):
          outputs = predict(
              batch[modalities.IMAGE], query_embeddings
          )  # pytype: disable=wrong-arg-types  # jax-ndarray
  
          # Selec top k predictions:
          scores, labels, boxes = pmapped_top_k(
              outputs[modalities.SCORES],
              outputs[modalities.PREDICTED_BOXES],
              top_k,
              exclusive_classes,
          )
  
          # Move to CPU:
          (
              scores,
              labels,
              boxes,
              image_sizes,
              image_ids,
          ) = _unshard_and_get(
              [
                  scores,
                  labels,
                  boxes,
                  batch[modalities.ORIGINAL_SIZE],
                  batch[modalities.IMAGE_ID],
              ]
          )
  
          # Append predictions:
          predictions.extend(
              format_predictions(
                  scores=scores,
                  labels=labels,
                  boxes=boxes,
                  image_sizes=image_sizes,
                  image_ids=image_ids,
                  label_shift=label_shift,
              )
          )
  
      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 9 for calling a global function with example usage: get_predictions(config, checkpoint_path, tfds_name, split, top_k, exclusive_classes, label_shift) and returns: predictions