code: def test_can_instantiate_n_dimensional(self):
      em = universal.UniversalIndexedEntropyModel(
          uniform_noise.NoisyLogisticMixture,
          index_ranges=(10, 10, 5),
          parameter_fns=dict(
              loc=lambda i: i[..., 0:2] - 5,
              scale=lambda _: 1,
              weight=lambda i: tf.nn.softmax(
                  (i[..., 2:3] - 2) * [-1, 1]
              ),
          ),
          coding_rank=1,
      )
      self.assertEqual(em.coding_rank, 1)
      self.assertEqual(em._laplace_tail_mass, 0.0)
      self.assertEqual(em.tail_mass, 2**-8)
      self.assertEqual(em.bottleneck_dtype, tf.float32)
      self.assertEqual(em.prior_dtype, tf.float32)
  
imports: from absl.testing import parameterized
  import scipy.stats
  import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import deep_factorized
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.entropy_models import universal
label_desc: Use image node_id 2 for calling the UniversalIndexedEntropyModelTest obj's underlying member method code with example usage: obj.test_can_instantiate_n_dimensional() without return types