code: def train_step(network, data):
      inputs = {
          "obs": data[:, :, :-1],
          "target": data[:, :, 1:],
      }
  
      loss, last_loss, grad_norm, grad_norm_micro = network.train(
          inputs
      )
  
      return (
          np.array(loss).mean(),
          np.array(last_loss).mean(),
          np.array(grad_norm).mean(),
          np.array(grad_norm_micro).mean(),
      )
  
file_path: mesh-transformer-jax/device_train.py
imports: import argparse
  import json
  import time
  import jax
  import numpy
  import optax
  import wandb
  from tqdm import tqdm
  from mesh_transformer import util
  from mesh_transformer.checkpoint import read_ckpt, write_ckpt
  from mesh_transformer.transformer_shard import CausalTransformer
  from tfrecord_loader import TFRecordNewInputs
  from smart_open import open
  from google.cloud import storage
  from google.cloud.exceptions import NotFound
  from mesh_transformer.util import clip_by_global_norm, additive_weight_decay
label_desc: Use image node_id 3 for calling a global function with example usage: train_step(network, data) and returns: np,  np,  np,  np