import torch
import torchvision.transforms as transforms
import torchvision
from tqdm import tqdm
#from utils.saver import Saver
from torchvision.utils import save_image
import pickle
def get_loaders(batch_size):
classes = ('beaver', 'dolphin', 'otter', 'seal','whale',
          'aquarium fish','flatfish','ray', 'shark', 'trout',
      'orchids', 'poppies','roses','sunflowers','tulips',
      'bottles',' bowls', 'cans', 'cups', 'plates',
      'apples',' mushrooms', 'oranges',' pears','sweet peppers',
      'clock','computer keyboard','lamp','telephone', 'television',
      'bed', 'chair','couch', 'table', 'wardrobe',
      'bee',' beetle','butterfly','caterpillar', 'cockroach',
      'bear', 'leopard', 'lion', 'tiger',' wolf',
          'bridge', 'castle', 'house','road', 'skyscraper',
      'cloud', 'forest', 'mountain', 'plain', 'sea',
      'camel','cattle','chimpanzee','elephant','kangaroo',
      'fox',' porcupine',' possum', 'raccoon',' skunk',
          'crab', 'lobster', 'snail', 'spider', 'worm',
      'baby', 'boy', 'girl', 'man',' woman',
      'crocodile', 'dinosaur', 'lizard',' snake', 'turtle',
      'hamster', 'mouse', 'rabbit', 'shrew','squirrel',
      'maple','oak', 'palm',' pine', 'willow',
      'bicycle', 'bus','motorcycle', 'pickup truck', 'train',
          'lawn-mower', 'rocket',' streetcar', 'tank', 'tractor')
transform = transforms.Compose(
    [   #transforms.Resize(224), #for alexnet
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
trainset = torchvision.datasets.CIFAR100(root='./data', train=True,
                         download=True, transform=transform)


testset = torchvision.datasets.CIFAR100(root='./data', train=False,
                        download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=4)


testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=4) 

                                         
dict=pickle.load(open('./data/cifar-100-python/meta','rb'))
label_names=dict
print(label_names)


#print(dict['coarse_label_names'])



return trainloader, testloader, classes,label_names,trainset

this is the file i had for the cifar100, i wonder if i can specify the labels inside Dataloader () type label_mode = ‘coarse_label’ or coarse label = True since the above dataloader nn allows me to have two labels.
assuming that the cifar10 train file contains a dictionary with data, labels, for the cifar 100 the train would contain data, labels and coarse_labels?