code: def _get_fake_detection_example(dataset="coco"):
      """Manually create a single example that has known results."""
      # A single box, values taken from ground-truth annotations and manually
      # converted to relative [cx, cy, w, h] format:
      h, w = 427, 640
      bx, by, bw, bh = 217.62, 240.54, 38.99, 57.75
  
      outputs = {}
      outputs["pred_boxes"] = np.array(
          [
              [(bx + bw / 2) / w, (by + bh / 2) / h, bw / w, bh / h],
          ]
      )
      outputs["pred_boxes"] = jnp.asarray(outputs["pred_boxes"])
      outputs["pred_logits"] = np.zeros((1, NUM_COCO_CLASSES))
      outputs["pred_logits"][0, 1] = 100.0
  
      targets = {}
      targets["size"] = np.array([h, w])
      targets["orig_size"] = np.array([h, w])
      targets["image/id"] = np.array([397133])
      targets["boxes"] = outputs["pred_boxes"]
      targets["is_crowd"] = np.array([0])
  
      if dataset == "coco":
          targets["labels"] = np.array([44])
      elif dataset == "lvis":
          targets["labels"] = np.array([1])
          targets["not_exhaustive_category_ids"] = np.array([])
          targets["neg_category_ids"] = np.array([2, 3])
      return outputs, targets
  
file_path: scenic/scenic/projects/baselines/detr/tests/test_train_utils.py
imports: import collections
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import jax
  import jax.numpy
  import numpy
  from scenic.projects.baselines.detr import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _get_fake_detection_example(dataset) and returns: outputs,  targets