class_docstr: Base Trainer class that provide ways to
      1 Train model, compute metrics against eval set and use the metrics for
      model selection.
      2 Test trained model, compute and publish metrics against a blind test set.
  
  Attributes:
      epochs (int): Training epochs
      early_stop_after (int): Stop after how many epochs when the eval metric
          is not improving
      max_clip_norm (Optional[float]): Clip gradient norm if set
      report_train_metrics (bool): Whether metrics on training data should be
          computed and reported.
      target_time_limit_seconds (float): Target time limit for training in seconds. If
          the expected time to train another epoch exceeds this limit, stop training.
docstr: Train and eval a model from a given training state will be modified.
  This function iterates epochs specified in config, and for each epoch do:
  
      1. Train model using training data, aggregate and report training results
      2. Adjust learning rate if scheduler is specified
      3. Evaluate model using evaluation data
      4. Calculate metrics based on evaluation results and select best model
  
  Args:
      training_state (TrainingState): contrains stateful information to be
      able to restore a training job
      train_iter (BatchIterator): batch iterator of training data
      eval_iter (BatchIterator): batch iterator of evaluation data
      model (Model): model to be trained
      metric_reporter (MetricReporter): compute metric based on training
          output and report results to console, file.. etc
      train_config (PyTextConfig): training config
  
  Returns:
      model, best_metric: the trained model together with the best metric
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code: def train_from_state(
      self,
      state: TrainingState,
      training_data: BatchIterator,
      eval_data: BatchIterator,
      metric_reporter: MetricReporter,
      train_config: PyTextConfig,
  ) -> Tuple[torch.nn.Module, Any]:
      """
      Train and eval a model from a given training state will be modified.
      This function iterates epochs specified in config, and for each epoch do:
  
          1. Train model using training data, aggregate and report training results
          2. Adjust learning rate if scheduler is specified
          3. Evaluate model using evaluation data
          4. Calculate metrics based on evaluation results and select best model
  
      Args:
          training_state (TrainingState): contrains stateful information to be
          able to restore a training job
          train_iter (BatchIterator): batch iterator of training data
          eval_iter (BatchIterator): batch iterator of evaluation data
          model (Model): model to be trained
          metric_reporter (MetricReporter): compute metric based on training
              output and report results to console, file.. etc
          train_config (PyTextConfig): training config
  
      Returns:
          model, best_metric: the trained model together with the best metric
      """
      training_data = self.set_up_training(state, training_data)
      model = state.model
      rank = state.rank
      trainable_params = sum(
          p.numel() for p in state.model.parameters() if p.requires_grad
      )
      print(f"Model :{model}")
      print(f"Num trainable parameters: {trainable_params}")
  
      self.sparsifier.initialize(
          self, state, eval_data, metric_reporter, train_config
      )
  
      while self.continue_training(state):
          self.sparsifier.op_pre_epoch(self, state)
          state.epoch += 1
          state.epochs_since_last_improvement += 1
          lrs = learning_rates(state.optimizer)
          print(f"\nWorker {state.rank} starting epoch {state.epoch}")
          print(f"Learning rate(s): {', '.join(map(str, lrs))}")
  
          with timing.time("train epoch"):
              state.stage = Stage.TRAIN
              state.model.train()
              if train_config.encoder_eval_mode and hasattr(
                  state.model, "encoder"
              ):
                  state.model.encoder.eval()
              print(f"start training epoch {state.epoch}")
              epoch_data = training_data
              if self.config.num_batches_per_epoch:
                  # We want to limit the number of batches in the epoch;
                  # equivalent to epoch_data[:num_batches_per_epoch] for iterators.
                  # In this case we set the training data iterator to cycle earlier
                  # in the training process, so when it reaches the end it will
                  # loop back to the beginning.
                  epoch_data = itertools.islice(
                      epoch_data, self.config.num_batches_per_epoch
                  )
              self.run_epoch(state, epoch_data, metric_reporter)
  
          if not self.config.do_eval:
              continue
  
          with timing.time("eval epoch"):
              state.stage = Stage.EVAL
              model.eval(Stage.EVAL)
              print(f"start evaluating epoch {state.epoch}")
              eval_metric = self.eval_from_state(
                  state, eval_data, metric_reporter
              )
  
          # Step the learning rate scheduler(s)
          assert eval_metric is not None
          state.scheduler.step_epoch(
              metrics=metric_reporter.get_model_select_metric(
                  eval_metric
              ),
              epoch=state.epoch,
          )
  
          # Did we train a better model?
          better_model = metric_reporter.compare_metric(
              eval_metric, state.best_model_metric
          )
          if better_model:
              self.update_best_model(state, train_config, eval_metric)
          if better_model or train_config.save_all_checkpoints:
              self.save_checkpoint(state, train_config)
  
      if self.optimizer.finalize():
          should_update_model = True
          eval_metric = None
          if self.config.do_eval:
              state.stage = Stage.EVAL
              model.eval(Stage.EVAL)
              print(f"start evaluating finalized state")
              with torch.no_grad():
                  eval_metric = self.eval_from_state(
                      state, eval_data, metric_reporter
                  )
              should_update_model = metric_reporter.compare_metric(
                  eval_metric, state.best_model_metric
              )
          if should_update_model:
              self.update_best_model(state, train_config, eval_metric)
          if should_update_model or train_config.save_all_checkpoints:
              self.save_checkpoint(state, train_config)
      # Only bother loading the best model for master worker
      if (
          rank == 0
          and state.best_model_state is not None
          and self.config.load_best_model_after_train
      ):
          self.load_best_model(state)
  
      metrics = state.best_model_metric
      if isinstance(metrics, AllMetrics):
          all_metrics = AllMetrics(
              metrics.top_intent_accuracy,
              metrics.frame_accuracy,
              metrics.frame_accuracy_top_k,
              metrics.frame_accuracies_by_depth,
              metrics.bracket_metrics,
              metrics.tree_metrics,
              metrics.percent_invalid_trees,
              metrics.percent_trees_wrong_label,
              metrics.loss,
              state.epoch,
          )
      else:
          all_metrics = state.best_model_metric
  
      return state.model, all_metrics
  
imports: import itertools
  import time
  from contextlib import ExitStack
  from typing import Any, Iterable, List, Optional, Tuple
  import torch
  from pytext.common.constants import BatchContext, Stage
  from pytext.config import PyTextConfig
  from pytext.config.component import Component, ComponentType, create_optimizer, create_scheduler, create_sparsifier
  from pytext.config.pytext_config import ConfigBase
  from pytext.data.data_handler import BatchIterator
  from pytext.metric_reporters import MetricReporter
  from pytext.metrics.intent_slot_metrics import AllMetrics
  from pytext.models.distributed_model import DistributedModel
  from pytext.models.model import Model
  from pytext.optimizer import Adam, learning_rates, Optimizer
  from pytext.optimizer.fp16_optimizer import FP16Optimizer, FP16OptimizerFairseq
  from pytext.optimizer.scheduler import Scheduler
  from pytext.optimizer.sparsifiers.sparsifier import Sparsifier
  from pytext.task.serialize import save
  from pytext.trainers.training_state import TrainingState
  from pytext.utils import cuda, distributed, precision, timing
  from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import fp16_compress_hook
label_desc: Use image node_id 17 for calling the Trainer obj's underlying member method code with example usage: obj.train_from_state(state, training_data, eval_data, metric_reporter, train_config) and returns: state,  all_metrics