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.
code: def eval_from_state(
      self,
      state: TrainingState,
      data: BatchIterator,
      metric_reporter: MetricReporter,
  ):
      with torch.no_grad():
          return self.run_epoch(state, data, metric_reporter)
  
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 3 for calling the Trainer obj's underlying member method code with example usage: obj.eval_from_state(state, data, metric_reporter) and returns: self