code: def multiple_samples_collate(batch, fold=False):
      """
      Collate function for repeated augmentation. Each instance in the batch has
      more than one sample.
      Args:
          batch (tuple or list): data batch to collate.
      Returns:
          (tuple): collated data batch.
      """
      inputs, labels, video_idx, time, extra_data = zip(*batch)
      inputs = [item for sublist in inputs for item in sublist]
      labels = [item for sublist in labels for item in sublist]
      video_idx = [item for sublist in video_idx for item in sublist]
      time = [item for sublist in time for item in sublist]
  
      inputs, labels, video_idx, time, extra_data = (
          default_collate(inputs),
          default_collate(labels),
          default_collate(video_idx),
          default_collate(time),
          default_collate(extra_data),
      )
      if fold:
          return [inputs], labels, video_idx, time, extra_data
      else:
          return inputs, labels, video_idx, time, extra_data
  
file_path: SlowFast/slowfast/datasets/loader.py
imports: import itertools
  import numpy
  from functools import partial
  from typing import List
  import torch
  from torch.utils.data._utils.collate import default_collate
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import RandomSampler, Sampler
  from slowfast.datasets.multigrid_helper import ShortCycleBatchSampler
  from .None import utils
  from .build import build_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: multiple_samples_collate(batch, fold) and returns: list,  labels,  video_idx,  time,  extra_data, inputs,  labels,  video_idx,  time,  extra_data