code: def maybe_accumulate_gradients(exit_stack, model, index, sample_size):
      # index == sample_size - 1 represents the last backward pass
      if (
          cuda.DISTRIBUTED_WORLD_SIZE > 1
          and hasattr(model, "no_sync")
          and index < sample_size - 1
      ):
          """
          Whenever *samples* contains more than one mini-batch (e.g sample_size > 1),
          we want to accumulate gradients locally and only call all-reduce in the
          last backwards pass.
          """
          exit_stack.enter_context(model.no_sync())
  
      if precision.FP16_ENABLED and index < sample_size - 1:
          """
          Whenever *samples* contains more than one mini-batch (e.g sample_size > 1),
          we want to accumulate gradients in FP16 parameters (e.g delay unscale)
          and only unscale to FP32 parameters after the last backward pass.
          """
          exit_stack.enter_context(precision.delay_unscale())
  
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 2 for calling a global function with example usage: maybe_accumulate_gradients(exit_stack, model, index, sample_size) without return types