class_docstr: A BNN with one hidden layer.
class_docstr_tok: ["A","BNN","with","one","hidden","layer","."]
code: def setup(self):
      if not hasattr(self, "input_warping"):
          self.input_warping = lambda x: x
      if not hasattr(self, "activation_function"):
          self.activation_function = nn.relu
      if not hasattr(self, "kernel_init"):
          self.kernel_init = initializers.lecun_normal()
      if not hasattr(self, "bias_init"):
          self.bias_init = initializers.zeros_init()
      self.dense1 = nn.Dense(
          self.width,
          kernel_init=self.kernel_init,
          bias_init=self.bias_init,
      )
      if not self.going_to_be_multiplied:
          self.dense2 = nn.Dense(
              self.likelihood_model.num_outputs(),
              kernel_init=nn.initializers.normal(
                  1.0 / jnp.sqrt(self.width)
              ),
              bias_init=nn.initializers.zeros,
          )
      else:
  
          def fake_dense2(x):
              out_shape = x.shape[:-1] + (
                  self.likelihood_model.num_outputs(),
              )
              return jnp.ones(out_shape)
  
          self.dense2 = fake_dense2
      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 OneLayerBNN obj's underlying member method code with example usage: obj.setup() and returns: jnp