code: def get_samples(self, shape, scale=2.0, dtype=tf.float32):
      # This produces samples from a smoothed Laplacian with the requested scale.
      # They're not really samples from the prior, but approximately cover it,
      # and have the same tail behavior.
      x = tf.random.stateless_uniform(
          shape, minval=0.0, maxval=1.0, seed=(0, 1), dtype=dtype
      )
      s = tf.random.stateless_uniform(
          shape, minval=-1.0, maxval=1.0, seed=(1, 2), dtype=dtype
      )
      u = tf.random.stateless_uniform(
          shape, minval=-0.5, maxval=0.5, seed=(3, 4), dtype=dtype
      )
      x = (tf.math.log(x) * tf.math.sign(s) + u) * scale
      indexes = tf.random.stateless_uniform(
          tuple(shape) + (3,),
          minval=-0.4,
          maxval=(2.4, 3.4, 5),
          seed=(5, 6),
          dtype=tf.float32,
      )
      return x, indexes
  
imports: from absl.testing import parameterized
  import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.entropy_models import continuous_indexed
label_desc: Use image node_id 2 for calling the ContinuousIndexedEntropyModelTest obj's underlying member method code with example usage: obj.get_samples(shape, scale, dtype) and returns: x,  indexes