class_docstr: A BNN corresponding to the Radial Basis Function kernel.
class_docstr_tok: ["A","BNN","corresponding","to","the","Radial","Basis","Function","kernel","."]
code: def distributions(self):
      d = super().distributions()
      return d | {
          "amplitude": lognormal_lib.LogNormal(
              loc=0, scale=self.amplitude_scale
          ),
          "length_scale": lognormal_lib.LogNormal(
              loc=0, scale=self.length_scale_scale
          ),
          "dense1": {
              "kernel": normal_lib.Normal(loc=0, scale=1.0),
              "bias": uniform_lib.Uniform(low=-jnp.pi, high=jnp.pi),
          },
      }
  
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 ExponentiatedQuadraticBNN obj's underlying member method code with example usage: obj.distributions() and returns: unknown