code: def get_model(
      self,
      prior_fn=uniform_noise.NoisyLogisticMixture,
      coding_rank=1,
      scale=1.0,
      **kwargs,
  ):
      return continuous_indexed.ContinuousIndexedEntropyModel(
          prior_fn,
          (2, 3, 5),
          dict(
              loc=lambda i: i[..., :2] - [0.0, 1.5],
              scale=lambda _: scale,
              weight=lambda i: tf.nn.softmax(
                  (i[..., 2:] - 2.0) * [-1.0, 1.0]
              ),
          ),
          coding_rank,
          **kwargs,
      )
  
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 1 for calling the ContinuousIndexedEntropyModelTest obj's underlying member method code with example usage: obj.get_model(prior_fn, coding_rank, scale) and returns: continuous_indexed