code: def _construct_loader_ffcv(
      dataset_name, split, batch_size, shuffle, drop_last
  ):
      """Constructs the data loader via ffcv for the given dataset."""
      err_str = "Dataset '{}' not supported".format(dataset_name)
      assert (
          dataset_name in _DATASETS and dataset_name in _PATHS
      ), err_str
      # Retrieve the data path for the dataset
      data_path = os.path.join(
          _DATA_DIR, "..", "ffcv", _PATHS[dataset_name]
      )
      # Construct the dataset
      dataset = _FFCV_DATASETS[dataset_name](data_path, split)
      # Create a loader
      dataset.construct_ffcv()
      loader = Loader(
          dataset.split_path,
          batch_size=batch_size,
          num_workers=cfg.DATA_LOADER.NUM_WORKERS,
          order=OrderOption.RANDOM
          if shuffle
          else OrderOption.SEQUENTIAL,
          os_cache=dataset.os_cache,
          drop_last=drop_last,
          pipelines=dataset.pipelines,
          distributed=dataset.distributed,
      )
      return loader
  
file_path: pycls/pycls/datasets/loader.py
imports: import os
  import torch
  from pycls.core.config import cfg
  from pycls.datasets.cifar10 import Cifar10
  from pycls.datasets.imagenet import ImageNet, ImageNetFFCV
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import RandomSampler
label_desc: Use image node_id 2 for calling a global function with example usage: _construct_loader_ffcv(dataset_name, split, batch_size, shuffle, drop_last) and returns: loader