code: def loss(self, x, targets, z_loss=1):
      x = f_psum(x)
      x = self.norm(x)
      logits = self.proj(x)
  
      shard_start_index = (
          jax.lax.axis_index("shard") * self.dim_per_shard
      )
      global_max = jax.lax.pmax(
          jax.lax.stop_gradient(logits.max(-1, keepdims=True)), "shard"
      )
      logits -= jax.lax.stop_gradient(global_max)
  
      gt_onehot = jax.nn.one_hot(
          targets - shard_start_index, self.dim_per_shard
      )
      predicted_logits = jnp.sum(
          jnp.multiply(gt_onehot, logits), axis=-1
      )
      predicted_logits = g_psum(predicted_logits)
  
      exp_logits = jnp.exp(logits)
  
      sum_exp_logits = exp_logits.sum(axis=-1)
      sum_exp_logits = g_psum(sum_exp_logits)
  
      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 ProjectionShard obj's underlying member method code with example usage: obj.loss(x, targets, z_loss) and returns: loss,  correct