code: def decode_coco_detection_example(example):
      """Given an instance and raw labels, creates <inputs, label> pair.
  
      Decoding includes.
      1. Converting images from uint8 [0, 255] to [0, 1.] float32.
      2. Mean subtraction and standardization using hard-coded mean and std.
      3. Convert boxes from yxyx [0-1] to xyxy un-normalized.
      4. Add 1 to all labels to account for background/padding object at label 0.
      5. Shuffling dictionary keys to be consistent with the rest of the code.
  
      Args:
        example: dict; Input image and raw labels.
  
      Returns:
        A dictionary of {'inputs': input image, 'labels': task label}.
      """
      image = tf.image.convert_image_dtype(
          example["image"], dtype=tf.float32
      )
  
      boxes = decode_boxes(
          example["objects"]["bbox"], tf.shape(image)[0:2]
      )
  
      target = {
          "area": example["objects"]["area"],
          "boxes": boxes,
          "objects/id": example["objects"]["id"],
          "is_crowd": example["objects"]["is_crowd"],
          "labels": example["objects"]["label"]
          + 1,  # 0'th class is padding.
      }
  
      # Filters objects to exclude degenerate boxes.
      keep = tf.where(
          tf.logical_and(
              boxes[:, 2] > boxes[:, 0], boxes[:, 3] > boxes[:, 1]
          )
      )[:, 0]
      target_kept = {k: tf.gather(v, keep) for k, v in target.items()}
  
      target_kept["orig_size"] = tf.cast(
          tf.shape(image)[0:2], dtype=tf.int32
      )
      target_kept["size"] = tf.identity(target_kept["orig_size"])
      target_kept["image/id"] = example["image/id"]
  
      return {
          "inputs": image,
          "label": target_kept,
      }
  
imports: import functools
  from typing import Any, Callable, Mapping, Optional
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib.coco_dataset import coco_utils
  from scenic.projects.baselines.detr import transforms
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 3 for calling a global function with example usage: decode_coco_detection_example(example) and returns: dict