code: def loss(self, x, targets, z_loss=1):
      x = self.norm(x)
      logits = self.proj(x)
  
      logits -= logits.max(-1, keepdims=True)
  
      gt_onehot = jax.nn.one_hot(targets, self.dim)
      predicted_logits = jnp.sum(
          jnp.multiply(gt_onehot, logits), axis=-1
      )
      exp_logits = jnp.exp(logits)
  
      sum_exp_logits = exp_logits.sum(axis=-1)
  
      loss = jnp.log(sum_exp_logits) - predicted_logits
  
      loss += (
          1e-4 * jnp.square(jnp.log(sum_exp_logits)) * z_loss
      ).mean()
      correct = 0.0 == predicted_logits
      return loss, correct
  
imports: import haiku
  import jax
  import jax.numpy
  import numpy
  from einops import rearrange, repeat
  from mesh_transformer.util import f_psum, g_psum, maybe_shard, head_print
  from jax.experimental import PartitionSpec
  from jax.experimental.maps import thread_resources
label_desc: Use image node_id 3 for calling the Projection obj's underlying member method code with example usage: obj.loss(x, targets, z_loss) and returns: loss,  correct