class_docstr: Multi-layer BNN that also has access to periodic features.
class_docstr_tok: ["Multi-layer","BNN","that","also","has","access","to","periodic","features","."]
code: def penultimate(self, inputs):
      y = self.input_warping(inputs)
      for i in range(self.num_layers):
          y = self.activation_function(self.dense[i](y))
      return y
  
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 MultiLayerBNN obj's underlying member method code with example usage: obj.penultimate(inputs) and returns: y