code: def concat_datasets(datasets):
      """
      Concatenates multiple datasets into a single dataset.
  
      It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support
      generic IterableDataset because it requires creating separate samplers.
  
      Now only supports conctenating training datasets and assuming validation and testing
      have only a single dataset. This is because metrics should not be computed on the concatenated
      datasets.
  
      Args:
          datasets: dict of torch.utils.data.Dataset objects by split.
  
      Returns:
          Dict of concatenated datasets by split, "train" is the concatenation of multiple datasets,
          "val" and "test" remain the same.
  
          If the input training datasets contain both map-style and DataPipeline datasets, returns
          a tuple, where the first element is a concatenated map-style dataset and the second
          element is a chained DataPipeline dataset.
  
      """
      # concatenate datasets in the same split
      for split_name in datasets:
          if split_name != "train":
              assert (
                  len(datasets[split_name]) == 1
              ), "Do not support multiple {} datasets.".format(
                  split_name
              )
              datasets[split_name] = datasets[split_name][0]
          else:
              iterable_datasets, map_datasets = [], []
              for dataset in datasets[split_name]:
                  if isinstance(dataset, wds.DataPipeline):
                      logging.info(
                          "Dataset {} is IterableDataset, can't be concatenated.".format(
                              dataset
                          )
                      )
                      iterable_datasets.append(dataset)
                  elif isinstance(dataset, IterableDataset):
                      raise NotImplementedError(
                          "Do not support concatenation of generic IterableDataset."
                      )
                  else:
                      map_datasets.append(dataset)
  
              # if len(iterable_datasets) > 0:
              # concatenate map-style datasets and iterable-style datasets separately
              chained_datasets = (
                  ChainDataset(iterable_datasets)
                  if len(iterable_datasets) > 0
                  else None
              )
              concat_datasets = (
                  ConcatDataset(map_datasets)
                  if len(map_datasets) > 0
                  else None
              )
  
              train_datasets = concat_datasets, chained_datasets
              train_datasets = tuple(
                  [x for x in train_datasets if x is not None]
              )
              train_datasets = (
                  train_datasets[0]
                  if len(train_datasets) == 1
                  else train_datasets
              )
  
              datasets[split_name] = train_datasets
  
      return datasets
  
imports: import gzip
  import logging
  import os
  import random
  import tarfile
  import zipfile
  import cv2
  import decord
  import webdataset
  import numpy
  import torch
  from torch.utils.data.dataset import IterableDataset, ChainDataset
  from decord import VideoReader
  from lavis.common.registry import registry
  from lavis.datasets.datasets.base_dataset import ConcatDataset
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: concat_datasets(datasets) and returns: datasets