code: def create_dataset(dataset, config, min_scale=0.5):
      normalize = transforms.Normalize(
          (0.48145466, 0.4578275, 0.40821073),
          (0.26862954, 0.26130258, 0.27577711),
      )
  
      transform_train = transforms.Compose(
          [
              transforms.RandomResizedCrop(
                  config["image_size"],
                  scale=(min_scale, 1.0),
                  interpolation=InterpolationMode.BICUBIC,
              ),
              transforms.RandomHorizontalFlip(),
              RandomAugment(
                  2,
                  5,
                  isPIL=True,
                  augs=[
                      "Identity",
                      "AutoContrast",
                      "Brightness",
                      "Sharpness",
                      "Equalize",
                      "ShearX",
                      "ShearY",
                      "TranslateX",
                      "TranslateY",
                      "Rotate",
                  ],
              ),
              transforms.ToTensor(),
              normalize,
          ]
      )
      transform_test = transforms.Compose(
          [
              transforms.Resize(
                  (config["image_size"], config["image_size"]),
                  interpolation=InterpolationMode.BICUBIC,
              ),
              transforms.ToTensor(),
              normalize,
          ]
      )
  
      if dataset == "pretrain":
          dataset = pretrain_dataset(
              config["train_file"],
              config["laion_path"],
              transform_train,
          )
          return dataset
  
      elif dataset == "caption_coco":
          train_dataset = coco_karpathy_train(
              transform_train,
              config["image_root"],
              config["ann_root"],
              prompt=config["prompt"],
          )
          val_dataset = coco_karpathy_caption_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "val",
          )
          test_dataset = coco_karpathy_caption_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "test",
          )
          return train_dataset, val_dataset, test_dataset
  
      elif dataset == "nocaps":
          val_dataset = nocaps_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "val",
          )
          test_dataset = nocaps_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "test",
          )
          return val_dataset, test_dataset
  
      elif dataset == "retrieval_coco":
          train_dataset = coco_karpathy_train(
              transform_train, config["image_root"], config["ann_root"]
          )
          val_dataset = coco_karpathy_retrieval_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "val",
          )
          test_dataset = coco_karpathy_retrieval_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "test",
          )
          return train_dataset, val_dataset, test_dataset
  
      elif dataset == "retrieval_flickr":
          train_dataset = flickr30k_train(
              transform_train, config["image_root"], config["ann_root"]
          )
          val_dataset = flickr30k_retrieval_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "val",
          )
          test_dataset = flickr30k_retrieval_eval(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "test",
          )
          return train_dataset, val_dataset, test_dataset
  
      elif dataset == "vqa":
          train_dataset = vqa_dataset(
              transform_train,
              config["ann_root"],
              config["vqa_root"],
              config["vg_root"],
              train_files=config["train_files"],
              split="train",
          )
          test_dataset = vqa_dataset(
              transform_test,
              config["ann_root"],
              config["vqa_root"],
              config["vg_root"],
              split="test",
          )
          return train_dataset, test_dataset
  
      elif dataset == "nlvr":
          train_dataset = nlvr_dataset(
              transform_train,
              config["image_root"],
              config["ann_root"],
              "train",
          )
          val_dataset = nlvr_dataset(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "val",
          )
          test_dataset = nlvr_dataset(
              transform_test,
              config["image_root"],
              config["ann_root"],
              "test",
          )
          return train_dataset, val_dataset, test_dataset
  
file_path: blip/data/__init__.py
imports: import torch
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.transforms.functional import InterpolationMode
  from data.coco_karpathy_dataset import coco_karpathy_train, coco_karpathy_caption_eval, coco_karpathy_retrieval_eval
  from data.nocaps_dataset import nocaps_eval
  from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval
  from data.vqa_dataset import vqa_dataset
  from data.nlvr_dataset import nlvr_dataset
  from data.pretrain_dataset import pretrain_dataset
  from transform.randaugment import RandomAugment
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataset(dataset, config, min_scale) and returns: dataset, train_dataset,  val_dataset,  test_dataset, val_dataset,  test_dataset, train_dataset,  val_dataset,  test_dataset, train_dataset,  val_dataset,  test_dataset, train_dataset,  test_dataset, train_dataset,  val_dataset,  test_dataset