code: def test_haiku_module_loss(self):
      def forward(x):
          return MyModel()(x)
  
      forward_t = transform.transform_with_state(forward)
  
      rng = jax.random.PRNGKey(42)
      x = jnp.zeros((16, 8, 8, 32))
      params, state = forward_t.init(rng, x)
  
      def loss(params, state, rng, x):
          loss = jnp.sum(forward_t.apply(params, state, rng, x)[0])
          return loss, loss
  
      grad = jax.grad(loss, has_aux=True)
      mod = jaxpr_info.make_model_info(grad)(params, state, rng, x)
      formatted_mod = jaxpr_info.format_module(mod)
  
      # Support old JAX versions on GitHub presubmits.
      formatted_mod = formatted_mod.replace(
          "transpose(jvp(conv2_d))", "conv2_d"
      ).replace("jvp(conv2_d)", "conv2_d")
  
      self.assertContentsEqual(
          formatted_mod,
          """
  loss
    jvp(my_model)
      conv2_d
        conv_general_dilated in f32[16,8,8,32], f32[3,3,32,16], out f32[16,8,8,16]
        broadcast_in_dim in f32[16], out f32[16,8,8,16]
        add in f32[16,8,8,16], f32[16,8,8,16], out f32[16,8,8,16]
    reduce_sum in f32[16,8,8,16], out f32[]
    broadcast_in_dim in f32[], out f32[16,8,8,16]
    transpose(jvp(my_model))
      conv2_d
        reduce_sum in f32[16,8,8,16], out f32[16]
        conv_general_dilated in f32[16,8,8,32], f32[16,8,8,16], out f32[3,3,32,16]
  """.strip(),
      )
  
imports: from typing import Optional
  from absl import logging
  from absl.testing import absltest
  from haiku._src import conv
  from haiku._src import jaxpr_info
  from haiku._src import module
  from haiku._src import transform
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 4 for calling the JaxprInfoTest obj's underlying member method code with example usage: obj.test_haiku_module_loss() and returns: MyModel, loss,  loss