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 distributions(self):
      d = super().distributions()
      del d["dense1"]
      for i in range(self.num_layers):
          d[f"dense_{i}"] = {
              "kernel": normal_lib.Normal(
                  loc=0, scale=1.0 / jnp.sqrt(self.width)
              ),
              "bias": normal_lib.Normal(loc=0, scale=self.bias_scale),
          }
      return d
  
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 2 for calling the MultiLayerBNN obj's underlying member method code with example usage: obj.distributions() and returns: d