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 run_epoch(
      self,
      state: TrainingState,
      data: BatchIterator,
      metric_reporter: MetricReporter,
  ):
      # This method is due for some refactoring, pushing it off because it interacts
      # with the metric reporter too much. Much of the logic here either changes in
      # the NewTaskTrainer or should change with a better metric reporter design.
      report_metric = (
          state.stage != Stage.TRAIN or self.config.report_train_metrics
      )
      model = state.model
      samples = []
      is_data_empty = True
  
      """
          Sometimes, a batch of inputs is too large to fit into GPU, which has to
          be split into several micro-batches. However, to improve efficiency,
          it would be helpful to only apply params/gradients sync at original batch
          boundaries instead of micro-batch boundaries.
          num_accumulated_batches specified the number of accumulating gradients
          locally before sync gradients, total training_batch_size =
          train_batch_size x num_accumulated_batches and it will improve the system
          performance by reduce the total network transfer bytes.
          """
      for sample in enumerate(data):
          is_data_empty = False
          samples.append(sample)
          if (
              state.stage != Stage.TRAIN
              or len(samples) == self.config.num_accumulated_batches
          ):
              self.run_step(
                  samples, state, metric_reporter, report_metric
              )
              samples = []
      if samples:
          self.run_step(samples, state, metric_reporter, report_metric)
          samples = []
  
      metrics = None
      if report_metric:
          if is_data_empty:
              error_msg = (
                  f"Trying to report metric for stage {state.stage}, but no data was "
                  "found. Either disable metric reporting for this stage, pass in "
                  "non-empty data, or see if data fields are misnamed (warnings "
                  "would appear in preceding stdout logs)."
              )
              raise ValueError(error_msg)
  
          if (
              hasattr(model, "get_model_size")
              and state.stage != Stage.TRAIN
          ):
              metric_reporter.all_context[
                  "current_model_parameter_size"
              ] = model.get_model_size()
  
          with timing.time("report metrics"):
              metrics = metric_reporter.report_metric(
                  model,
                  state.stage,
                  state.epoch,
                  print_to_channels=(state.rank == 0),
                  optimizer=getattr(
                      state, "optimizer", None
                  ),  # optimizer is not present during test
              )
      else:
          metric_reporter._reset()
  
      if state.rank == 0 and self.config.sparsifier:
          current_sparsity = self.sparsifier.get_current_sparsity(
              state.model
          )
          print(f"sparsity in the model: {current_sparsity}")
  
      return 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 18 for calling the Trainer obj's underlying member method code with example usage: obj.run_epoch(state, data, metric_reporter) and returns: metrics