code: def fake_boxes(n, h, w):
      """Sample boxes in [x, y, x, y] format in un-normalized image coordinates."""
      h, w = float(h), float(w)
  
      # samples boxes in [0, 1]
      boxes = tf.random.uniform(
          (n, 4), minval=0.0, maxval=1.0, dtype=tf.float32
      )
  
      # ensures that x0 < x1, y0 < y1, and un-normalizes boxes
      x0 = tf.minimum(boxes[:, 0], boxes[:, 2]) * h
      x1 = tf.maximum(boxes[:, 0], boxes[:, 2]) * w
      y0 = tf.minimum(boxes[:, 1], boxes[:, 3]) * h
      y1 = tf.maximum(boxes[:, 1], boxes[:, 3]) * w
  
      area = (x1 - x0) * (y1 - y0)
  
      return tf.stack([x0, y0, x1, y1], axis=-1), area
  
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 1 for calling a global function with example usage: fake_boxes(n, h, w) and returns: tf,  area