code: def test_pugail(self):
      def dummy_discriminator(
          state: losses.State, transition: types.Transition
      ) -> losses.DiscriminatorOutput:
          return transition.observation, state
  
      zero_transition = types.Transition(0.1, 0.0, 0.0, 0.0, 0.0)
      zero_transition = tree.map_structure(
          lambda x: jnp.expand_dims(x, axis=0), zero_transition
      )
  
      one_transition = types.Transition(1.0, 0.0, 0.0, 0.0, 0.0)
      one_transition = tree.map_structure(
          lambda x: jnp.expand_dims(x, axis=0), one_transition
      )
  
      prior = 0.7
      loss_fn = losses.pugail_loss(
          positive_class_prior=prior, entropy_coefficient=0.0
      )
      loss, _ = loss_fn(  # pytype: disable=wrong-arg-types
          dummy_discriminator,
          {},
          one_transition,
          zero_transition,
          (),
      )
  
      d_one = jax.nn.sigmoid(dummy_discriminator({}, one_transition)[0])
      d_zero = jax.nn.sigmoid(
          dummy_discriminator({}, zero_transition)[0]
      )
      expected_loss = (
          -prior * jnp.log(d_one)
          + -jnp.log(1.0 - d_zero)
          - prior * -jnp.log(1 - d_one)
      )
  
      self.assertAlmostEqual(loss, expected_loss, places=6)
  
file_path: acme/acme/agents/jax/ail/losses_test.py
imports: from acme import types
  from acme.agents.jax.ail import losses
  from acme.jax import networks
  import jax
  import jax.numpy
  import tree
  from absl.testing import absltest
label_desc: Use image node_id 2 for calling the AilLossTest obj's underlying member method code with example usage: obj.test_pugail() and returns: transition,  state