code: def test_penalty_is_nonnegative_and_differentiable(self):
      em = PowerLawEntropyModel(coding_rank=1)
      x = tf.range(-20.0, 20.0)[:, None]
      x += tf.random.uniform(x.shape, -0.49, 0.49)
      with tf.GradientTape() as tape:
          tape.watch(x)
          penalties = em.penalty(x)
      gradients = tape.gradient(penalties, x)
      self.assertAllGreaterEqual(penalties, 0)
      self.assertAllEqual(tf.sign(gradients), tf.sign(x))
  
file_path: compression/tensorflow_compression/python/entropy_models/power_law_test.py
imports: import numpy
  import tensorflow
  from tensorflow_compression.python.entropy_models.power_law import PowerLawEntropyModel
label_desc: Use image node_id 7 for calling the PowerLawEntropyModelTest obj's underlying member method code with example usage: obj.test_penalty_is_nonnegative_and_differentiable() without return types