code: def fake_decoded_features(n, h, w):
      """Create a fake features dictionary.
  
      Args:
        n: int; number of boxes/objects.
        h: int; height of fake image.
        w: int; width of fake image.
  
      Returns:
        Features dictionary with fields -
          'inputs': tf.Tensor; float32 [H x W x 3] decoded mean-subtracted image.
          'label': dict; target labels dict with keys -
            'boxes': tf.Tensor; float32 [n-objects x 4] in xyxy unnormalized format.
            'labels': tf.Tensor; int32 labels.
            'area': tf.Tensor; float32 [n-objects,] object box area.
            'is_crowd': tf.Tensor; bool [n-objects,] object is crowd type or not.
            'objects/id': tf.Tensor; int32 [n-objects,] object identifier.
      """
      inputs = tf.random.normal((h, w, 3), dtype=tf.float32)
      boxes, area = fake_boxes(n, h, w)
  
      is_crowd = [False for _ in range(n)]
      is_crowd[-1] = True
  
      label = {
          "boxes": boxes,
          "labels": tf.range(7),
          "area": area,
          "is_crowd": tf.constant(is_crowd, dtype=tf.bool),
          "objects/id": tf.random.uniform(
              [
                  n,
              ],
              0,
              1024,
              dtype=tf.int32,
          ),
      }
      return {"inputs": inputs, "label": label}
  
imports: import copy
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from scenic.projects.baselines.detr import transforms
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: fake_decoded_features(n, h, w) and returns: dict