def build_dataloader()

in src/transformers/data/datasets/kinetics_datasets.py [0:0]
33 lines of code
8 McCabe index (conditional complexity)

def build_dataloader(cfg):
    # build dataset
    train_dataset = VideoDataset(mode='train', clip_len=cfg.CONFIG.DATA.CLIP_LEN,
                                 frame_sample_rate=cfg.CONFIG.DATA.FRAME_RATE,
                                 video_dir=cfg.CONFIG.DATA.TRAIN_DATA_PATH,
                                 label_dir=cfg.CONFIG.DATA.TRAIN_ANNO_PATH)
    if not cfg.CONFIG.TEST.MULTI_VIEW_TEST:
        # for validation during training
        val_dataset = VideoDataset(mode='validation', clip_len=cfg.CONFIG.DATA.CLIP_LEN,
                                   frame_sample_rate=cfg.CONFIG.DATA.FRAME_RATE,
                                   video_dir=cfg.CONFIG.DATA.VAL_DATA_PATH,
                                   label_dir=cfg.CONFIG.DATA.VAL_ANNO_PATH)
    else:
        # for multiview test
        val_dataset = VideoDataset(mode='test', clip_len=cfg.CONFIG.DATA.CLIP_LEN,
                                   frame_sample_rate=cfg.CONFIG.DATA.FRAME_RATE,
                                   video_dir=cfg.CONFIG.DATA.VAL_DATA_PATH,
                                   label_dir=cfg.CONFIG.DATA.VAL_ANNO_PATH)

    # build data sampler
    if cfg.DDP_CONFIG.DISTRIBUTED:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    else:
        train_sampler = None
        val_sampler = None

    # build data loader
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=cfg.CONFIG.TRAIN.BATCH_SIZE, shuffle=train_sampler is None,
        num_workers=9, sampler=train_sampler, pin_memory=True)

    if not cfg.CONFIG.TEST.MULTI_VIEW_TEST:
        # for validation during training
        val_loader = torch.utils.data.DataLoader(
            val_dataset, batch_size=cfg.CONFIG.VAL.BATCH_SIZE, shuffle=(val_sampler is None),
            num_workers=9, sampler=val_sampler, pin_memory=True)
    else:
        # for multiview test
        val_loader = torch.utils.data.DataLoader(
            val_dataset, batch_size=cfg.CONFIG.TEST.BATCH_SIZE, shuffle=(val_sampler is None),
            num_workers=9, sampler=val_sampler, pin_memory=True)

    return train_loader, val_loader, train_sampler, val_sampler, None