docstr: Our run_step is a bit different, because we're wrapping the model forward
  call with model.train_batch, which arranges tensors and gets loss, etc.
  
  Whenever "samples" contains more than one mini-batch (sample_size > 1),
  we want to accumulate gradients locally and only call all-reduce in the
  last backwards pass.
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code: def run_step(
      self,
      samples: List[Any],
      state: TrainingState,
      metric_reporter: MetricReporter,
      report_metric: bool,
  ):
      """Our run_step is a bit different, because we're wrapping the model forward
      call with model.train_batch, which arranges tensors and gets loss, etc.
  
      Whenever "samples" contains more than one mini-batch (sample_size > 1),
      we want to accumulate gradients locally and only call all-reduce in the
      last backwards pass.
      """
      sample_size = len(samples)
      assert sample_size <= self.config.num_accumulated_batches
  
      model = state.model
      self.zero_grads(state)
      for idx, (batch_id, (raw_batch, batch)) in enumerate(samples):
          with contextlib_ExitStack() as exit_stack:
              # enter ddp no_sync context and fp16 delay_scale context if needed
              maybe_accumulate_gradients(
                  exit_stack, model, idx, sample_size
              )
              with timing.time("model.train_batch"):
                  loss, metric_data = model.train_batch(
                      model, batch, state
                  )
                  if sample_size > 1:
                      # gradients averaged per batch and accumulated across samples.
                      # divide sample_size to let gradients averaged per example
                      loss = loss / sample_size
              self.backprop(state, loss)
  
          if report_metric:
              with timing.time("add metrics"):
                  metric_reporter.add_batch_stats(
                      batch_id,
                      *metric_data,
                      # TODO merge this step into add_batch_stats once all data
                      # migration is done
                      **metric_reporter.batch_context(raw_batch, batch),
                  )
              if (
                  batch_id % self.config.num_samples_to_log_progress
                  == 0
              ):
                  metric_reporter.report_realtime_metric(state.stage)
      # update gradients after #len(samples) forward & backward
      self.optimizer_step(state)
      with timing.time("add gradients"):
          if report_metric and state.stage == Stage.TRAIN:
              metric_reporter.add_gradients(state.model)
      self.sparsification_step(state)
  
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 1 for calling the TaskTrainer obj's underlying member method code with example usage: obj.run_step(samples, state, metric_reporter, report_metric) without return types