class_docstr: A BNN where samples are polynomial functions.
class_docstr_tok: ["A","BNN","where","samples","are","polynomial","functions","."]
code: def penultimate(self, inputs):
      x = inputs - self.shift
      ys = jnp.stack([h(x) for h in self.hiddens], axis=-1)
      return self.amplitude * jnp.prod(ys, axis=-1)
  
imports: from flax import linen
  from flax.linen import initializers
  import jax
  import jax.numpy
  from tensorflow_probability.python.experimental.autobnn import bnn
  from tensorflow_probability.substrates.jax.distributions import lognormal
  from tensorflow_probability.substrates.jax.distributions import normal
  from tensorflow_probability.substrates.jax.distributions import student_t
  from tensorflow_probability.substrates.jax.distributions import uniform
label_desc: Use image node_id 3 for calling the PolynomialBNN obj's underlying member method code with example usage: obj.penultimate(inputs) and returns: unknown