code: def loss(self, ctx, tgt, z_loss=False, mask=0.0):
      loss, correct = self.eval(ctx, tgt, float(z_loss), mask=mask)
  
      return {
          "loss": loss.mean(),
          "last_loss": loss[-1].mean(),
          "all_loss": loss,
          "correct": correct,
      }
  
imports: import gc
  import random
  import time
  from functools import partial
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  from jax.experimental.maps import thread_resources
  from jax.experimental.pjit import pjit
  from mesh_transformer.checkpoint import read_ckpt, write_ckpt, write_ckpt_v2, load_ckpt_v2
  from mesh_transformer.layers import EmbeddingShard, TransformerLayerShard, RelativePositionEmbs, ProjectionShard, TransformerLayerShardV2, Projection, EmbeddingShardV2
  from mesh_transformer.util import to_f32, to_bf16, maybe_shard, head_print, global_norm
  from jax.experimental import PartitionSpec
label_desc: Use image node_id 3 for calling the CausalTransformerShard obj's underlying member method code with example usage: obj.loss(ctx, tgt, z_loss, mask) and returns: dict