code: def get_fake_batch_output():
      """Generates a fake `batch`.
  
      Returns:
        `batch`: Dictionary of None inputs and fake ground truth targets.
            outputs_noaux.pop('aux_outputs')
        `output`: Dictionary of a fake output logits.
      """
      batch = {
          "inputs": None,
          "label": jnp.array(
              np.random.randint(
                  NUM_CLASSES, size=(BATCH_SIZE, HEIGHT, WIDTH)
              )
          ),
      }
      output = np.random.random(
          size=(BATCH_SIZE, HEIGHT, WIDTH, NUM_CLASSES)
      )
      all_confusion_mat = [
          np.random.random(size=(BATCH_SIZE, NUM_CLASSES, NUM_CLASSES))
      ]
      return batch, output, all_confusion_mat
  
file_path: scenic/scenic/model_lib/base_models/tests/test_segmentation_model.py
imports: from absl.testing import absltest
  from flax import jax_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models import segmentation_model
label_desc: Use image node_id 1 for calling a global function with example usage: get_fake_batch_output() and returns: batch,  output,  all_confusion_mat