code: def eval(self, context, target, z_loss=0.0, mask=0.0):
      input_len = context.shape[0]
  
      if self.rpe is not None:
          attn_bias = self.rpe(
              input_len, input_len, self.heads_per_shard, 32
          )
      else:
          attn_bias = 0
  
      attn_bias += mask
  
      x = hk.remat(self.embed)(context)
  
      for l in self.transformer_layers:
          x = x + hk.remat(l)(x, attn_bias)
  
      return hk.remat(self.proj.loss)(x, target, z_loss)
  
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 2 for calling the CausalTransformerShard obj's underlying member method code with example usage: obj.eval(context, target, z_loss, mask) and returns: hk