code: def train(self, sample):
      # print("train iter")
      # print("sample", sample["obs"])
      # print("target", sample["target"])
  
      obs = sample["obs"]
      target = sample["target"]
  
      if self.eval_weights is not None:
          self.eval_weights = None
          gc.collect()
          head_print("deleted eval weights")
  
      # print("train sample", obs.shape)
      # print("train target", target.shape)
  
      # assert (sample["obs"][:, 1:] == sample["target"][:, -1])
  
      start = time.time()
      loss, last_loss, self.state = self.train_pjit(
          self.state, obs, target
      )
      loss = np.array(loss)
      last_loss = np.array(last_loss)
  
      # head_print(f"iter done in {time.time() - start:.06}s")
      return loss.mean(), last_loss.mean()
  
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 4 for calling the CausalTransformerV2 obj's underlying member method code with example usage: obj.train(sample) and returns: loss,  last_loss