class_docstr: Switch from one BNN to another based on a time point.
code: def __call__(self, inputs, deterministic=True):
      time = inputs[..., self.change_index, jnp.newaxis]
      y = (time - self.change_point) / self.change_slope
      return nn.sigmoid(y) * self.bnns[1](inputs) + nn.sigmoid(
          -y
      ) * self.bnns[0](inputs)
  
imports: from typing import Optional
  from flax import linen
  import jax.numpy
  from tensorflow_probability.python.experimental.autobnn import bnn
  from tensorflow_probability.python.experimental.autobnn import likelihoods
  from tensorflow_probability.substrates.jax.bijectors import chain
  from tensorflow_probability.substrates.jax.bijectors import scale
  from tensorflow_probability.substrates.jax.bijectors import shift
  from tensorflow_probability.substrates.jax.distributions import beta
  from tensorflow_probability.substrates.jax.distributions import dirichlet
  from tensorflow_probability.substrates.jax.distributions import half_normal
  from tensorflow_probability.substrates.jax.distributions import normal
  from tensorflow_probability.substrates.jax.distributions import transformed_distribution
label_desc: Use image node_id 3 for calling the LearnableChangePoint obj's underlying member method code with example usage: obj.__call__(inputs, deterministic) and returns: unknown