code: def train(
      output_dir,
      model=gin.REQUIRED,
      loss_fn=tl.WeightedCategoryCrossEntropy(),
      inputs=trax_inputs.batcher,
      optimizer=trax_opt.Adafactor,
      lr_schedule_fn=lr.multifactor,
      trainer_class=Trainer,
      steps=1000,
      checkpoints_at=None,
      permanent_checkpoints_at=None,
      eval_steps=10,
      eval_frequency=100,
      permanent_checkpoint_frequency=None,
      random_seed=None,
      save_graphs=True,
      metrics=None,
      checkpoint_highest=None,
      checkpoint_lowest=None,
      use_loop=True,
      loss_chunk_size=0,
      use_memory_efficient_trainer=False,
      adasum=False,
      init_checkpoint=None,
      callbacks=None,
      n_weights_shards=1,
      additional_train_tasks=None,
      additional_eval_tasks=None,
      additional_eval_streams=None,
  ):
      """Train the model on the inputs.
  
      Args:
        output_dir: Directory where to put the logs and checkpoints.
        model: The model to train as a callable returning 2 callables, an init_fn
          and apply_fn.
        loss_fn: callable with signature: weights, trax.inputs.Inputs, model, state,
          rng -> loss.
        inputs: callable returning trax.inputs.Inputs.
        optimizer: The optimizer (see optimizers/base.py for signature).
        lr_schedule_fn: A learning rate schedule function, that when called returns
          a function from step to learning rate (a float).
        trainer_class: The trainer class to use.
        steps: int, total number of training steps.
        checkpoints_at: list of integers. Save a checkpoint for each training step
          in the list.
        permanent_checkpoints_at: list of integers. Save a permanent checkpoint for
          each training step in the list.
        eval_steps: int, num of steps per evaluation. If None or 0, eval disabled.
        eval_frequency: int, how often to run evaluation (every eval_frequency
          steps). If None or 0, eval disabled.
        permanent_checkpoint_frequency: int, how often to save permanent checkpoints
          (every permanent_checkpoint_frequency steps).
        random_seed: the random seed to use; time/os dependent if None (default).
        save_graphs: bool, if True, save computation graph to file.
        metrics: optionally override the default metrics dictionary.
        checkpoint_highest: save the checkpoint highest at this metric.
        checkpoint_lowest: save the checkpoint lowest at this metric.
        use_loop: whether to use training.Loop instead of Trainer.
        loss_chunk_size: int, if > 0 chunk loss into these sizes to save memory.
        use_memory_efficient_trainer: whether to use memory-efficient trainer.
        adasum: if True, use adaptive summation for multi-device gradients.
        init_checkpoint: a checkpoint for fine tuning.
        callbacks: a list of callbacks to call during training.
        n_weights_shards: shard weights into this many devices.
        additional_train_tasks: additional tasks which should be performed during
          training.
        additional_eval_tasks: additional tasks which should be performed during
          evaluation.
        additional_eval_streams: List[NamedStream], additional data streams that
          should be used during evaluation. Can be provided independently of
          additional_eval_tasks.
  
      Returns:
        trax.TrainerState or training.Loop if use_loop is True
      """
      base.N_WEIGHTS_SHARDS = n_weights_shards
      if (
          permanent_checkpoint_frequency is not None
          and permanent_checkpoints_at is not None
      ):
          raise ValueError(
              'Only one of ["permanent_checkpoint_frequency", '
              '"permanent_checkpoints_at"] should be set.'
          )
      if use_loop:
          n_devices = num_devices() or fastmath.local_device_count()
  
          # Prepare the training task.
          # Inputs is either an Inputs instance or a function that returns it.
          if callable(
              inputs
          ):  # If we pass a function, e.g., through gin, call it.
              inputs = inputs()
          opt = (
              optimizer if use_memory_efficient_trainer else optimizer()
          )
          train_task = training.TrainTask(
              inputs.train_stream(n_devices),
              loss_layer=loss_fn,
              optimizer=opt,
              lr_schedule=lr_schedule_fn(),
              n_steps_per_checkpoint=eval_frequency,
              n_steps_per_permanent_checkpoint=permanent_checkpoint_frequency,
          )
  
          if additional_train_tasks is None:
              additional_train_tasks = []
  
          # Prepare the evaluation.
          metrics_dict = (
              metrics if metrics is not None else _DEFAULT_METRICS
          )
          names, metrics = zip(*metrics_dict.items())
          eval_task = training.EvalTask(
              inputs.eval_stream(n_devices),
              metrics,
              metric_names=names,
              n_eval_batches=eval_steps,
          )
  
          if additional_eval_tasks is None:
              additional_eval_tasks = []
  
          additional_eval_tasks_from_streams = []
          if additional_eval_streams is not None:
              for stream in additional_eval_streams:
                  additional_eval_tasks_from_streams.append(
                      training.EvalTask(
                          stream.stream,
                          metrics,
                          metric_names=names,
                          n_eval_batches=eval_steps,
                          export_prefix=stream.name,
                      )
                  )
  
          # Prepare the training loop.
          checkpoint_at = None
          if checkpoints_at is not None:
              checkpoint_at = lambda step: step in checkpoints_at
          permanent_checkpoint_at = None
          if permanent_checkpoints_at is not None:
              permanent_checkpoint_at = (
                  lambda step: step in permanent_checkpoints_at
              )
  
          # Setup the model.
          model_train = model(mode="train")
          model_predict_eval = model(mode="eval")
          if init_checkpoint:
              model_train.init_from_file(
                  init_checkpoint, weights_only=True
              )
              model_predict_eval.init_from_file(
                  init_checkpoint, weights_only=True
              )
          loop = training.Loop(
              model_train,
              [train_task] + additional_train_tasks,
              eval_model=model_predict_eval,
              eval_tasks=[eval_task]
              + additional_eval_tasks
              + additional_eval_tasks_from_streams,
              output_dir=output_dir,
              checkpoint_at=checkpoint_at,
              checkpoint_low_metric=checkpoint_lowest,
              checkpoint_high_metric=checkpoint_highest,
              permanent_checkpoint_at=permanent_checkpoint_at,
              n_devices=n_devices,
              loss_chunk_size=loss_chunk_size,
              use_memory_efficient_trainer=use_memory_efficient_trainer,
              adasum=adasum,
              random_seed=random_seed,
              callbacks=callbacks,
          )
  
          steps_to_go = steps - loop.step
          if steps_to_go <= 0:
              log(
                  "Stop training, already reached the total training steps %d"
                  % steps
              )
              return loop
  
          # Train and return the loop.
          loop.run(steps_to_go)
          return loop
  
      n_devices = num_devices()
      trainer = trainer_class(
          model,
          loss_fn,
          optimizer,
          lr_schedule_fn(),
          inputs,
          output_dir,
          random_seed=random_seed,
          n_devices=n_devices,
          checkpoints_at=checkpoints_at,
          metrics=metrics,
          checkpoint_lowest=checkpoint_lowest,
          checkpoint_highest=checkpoint_highest,
          init_checkpoint=init_checkpoint,
      )
  
      epoch_steps = [
          steps
      ]  # Only training if eval_frequency is 0 or None
      if eval_frequency and eval_steps > 0:
          epoch_steps = itertools.chain(
              [1, eval_frequency - 1],  # first epoch only 1 step
              itertools.repeat(eval_frequency),
          )
      trainer.log_step(
          "Starting training using %d devices" % trainer.n_devices
      )
      trainer.print_n_weights()
  
      try:
          for epoch_steps in epochs(steps, trainer.step, epoch_steps):
              trainer.train_epoch(epoch_steps, eval_steps)
  
              # Bookkeeping we do at the first step
              if trainer.step == 1:
                  # Save computation graph (single-device only for now)
                  if save_graphs and fastmath.is_backend(
                      fastmath.Backend.JAX
                  ):
                      trainer.save_computation_graphs()
  
                  # Save Gin config
                  trainer.save_gin()
  
          trainer.log_step("Training done")
      except Exception as e:
          raise e
      finally:
          trainer.close()
      return trainer.state
  
imports: import collections
  import functools
  import itertools
  import os
  import sys
  import time
  from absl import logging
  import gin
  import jax
  import tensorflow.compat.v2
  from trax import fastmath
  from trax import jaxboard
  from trax import layers
  from trax import optimizers
  from trax.data import inputs
  from trax.fastmath import numpy
  from trax.fastmath import random
  from trax.layers import base
  from trax.shapes import ShapeDtype
  from trax.supervised import history
  from trax.supervised import lr_schedules
  from trax.supervised import training
label_desc: Use image node_id 2 for calling a global function with example usage: train(output_dir, model, loss_fn, inputs, optimizer, lr_schedule_fn, trainer_class, steps, checkpoints_at, permanent_checkpoints_at, eval_steps, eval_frequency, permanent_checkpoint_frequency, random_seed, save_graphs, metrics, checkpoint_highest, checkpoint_lowest, use_loop, loss_chunk_size, use_memory_efficient_trainer, adasum, init_checkpoint, callbacks, n_weights_shards, additional_train_tasks, additional_eval_tasks, additional_eval_streams) and returns: trainer, loop, loop