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 setup(self):
      if not hasattr(self, "activation_function"):
          self.activation_function = lambda x: SQRT_TWO * jnp.sin(x)
      if not hasattr(self, "input_warping"):
          self.input_warping = lambda x: x / self.length_scale
      self.kernel_init = nn.initializers.normal(1.0)
  
      def uniform_init(seed, shape, dtype):
          return (
              nn.initializers.uniform(scale=2.0 * jnp.pi)(
                  seed, shape, dtype=dtype
              )
              - jnp.pi
          )
  
      self.bias_init = uniform_init
      super().setup()
  
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 1 for calling the ExponentiatedQuadraticBNN obj's underlying member method code with example usage: obj.setup() and returns: unknown