code: def calculate_and_update_precise_bn(
      loader, model, num_iters=200, use_gpu=True
  ):
      """
      Update the stats in bn layers by calculate the precise stats.
      Args:
          loader (loader): data loader to provide training data.
          model (model): model to update the bn stats.
          num_iters (int): number of iterations to compute and update the bn stats.
          use_gpu (bool): whether to use GPU or not.
      """
  
      def _gen_loader():
          for inputs, *_ in loader:
              if use_gpu:
                  if isinstance(inputs, (list,)):
                      for i in range(len(inputs)):
                          inputs[i] = inputs[i].cuda(non_blocking=True)
                  else:
                      inputs = inputs.cuda(non_blocking=True)
              yield inputs
  
      # Update the bn stats.
      update_bn_stats(model, _gen_loader(), num_iters)
  
imports: import math
  import numpy
  import pprint
  import torch
  from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
  import slowfast.models.losses
  import slowfast.models.optimizer
  import slowfast.utils.checkpoint
  import slowfast.utils.distributed
  import slowfast.utils.logging
  import slowfast.utils.metrics
  import slowfast.utils.misc
  import slowfast.visualization.tensorboard_vis
  from slowfast.datasets import loader
  from slowfast.datasets.mixup import MixUp
  from slowfast.models import build_model
  from slowfast.models.contrastive import contrastive_forward, contrastive_parameter_surgery
  from slowfast.utils.meters import AVAMeter, EpochTimer, TrainMeter, ValMeter
  from slowfast.utils.multigrid import MultigridSchedule
label_desc: Use image node_id 3 for calling a global function with example usage: calculate_and_update_precise_bn(loader, model, num_iters, use_gpu) without return types