code: def fake_model_outputs_batch(num_boxes):
      """Generate fake data that resembles model `outputs` `batch` `indices`.
  
      See ObjectDetectionWithMatchingModel's loss_* functions for more details
      regarding the format of these dictionaries. The batch-size is hard-coded to
      3 because `indices` are manually synthesized.
  
      Args:
        num_boxes: int; Number of boxes in the data.
  
      Returns:
        `outputs`: dict; Dictionary of model predictions.
        `batch`: dict; Dictionary of None inputs and fake ground truth targets.
        `indices`: nd-array; Indices matching the structure returned by a matcher.
      """
      np.random.seed(num_boxes)
  
      outputs = {
          "pred_logits": jnp.array(
              np.random.normal(size=(3, num_boxes, NUM_CLASSES)),
              dtype=jnp.float32,
          ),
          "pred_boxes": jnp.array(
              np.random.uniform(
                  size=(3, num_boxes, 4), low=0.0, high=1.0
              ),
              dtype=jnp.float32,
          ),
          "pred_masks": jnp.array(
              np.random.uniform(
                  size=(3, num_boxes, 8, 8), low=0.0, high=1.0
              ),
              dtype=jnp.float32,
          ),
      }
      aux_outputs = [dict(outputs), dict(outputs)]
      outputs["aux_outputs"] = aux_outputs
      batch = {
          "inputs": None,
          "label": {
              "labels": jnp.array(
                  np.random.randint(NUM_CLASSES, size=(3, num_boxes))
              ),
              "boxes": jnp.array(
                  np.random.uniform(
                      size=(3, num_boxes, 4), low=0.0, high=1.0
                  ),
                  dtype=jnp.float32,
              ),
              "masks": jnp.array(
                  np.argsort(
                      np.random.uniform(size=(3, num_boxes, 16, 16)),
                      axis=1,
                  )
                  == 0,
                  dtype=jnp.float32,
              ),
              "image/id": jnp.array([87038, 348881, 143931]),
              "orig_size": jnp.array(
                  np.random.uniform(size=(3, 2), low=1, high=100),
                  dtype=jnp.int32,
              ),
          },
      }
  
      seq = np.arange(num_boxes, dtype=np.int32)
      seq_rev = seq[::-1]
      seq_21 = np.concatenate(
          [seq[num_boxes // 2 :], seq[: num_boxes // 2]]
      )
      indices = np.array(
          [(seq, seq_rev), (seq_rev, seq), (seq, seq_21)]
      )
  
      return outputs, batch, indices
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from flax import jax_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.projects.baselines.detr import detr_base_model
label_desc: Use image node_id 1 for calling a global function with example usage: fake_model_outputs_batch(num_boxes) and returns: outputs,  batch,  indices