# ================================================================== #
#                Input pipeline for custom dataset                 #
# ================================================================== #

# You should build your custom dataset as below.
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self):
        # TODO
        # 1. Initialize file paths or a list of file names. 
        pass
    def __getitem__(self, index):
        # TODO
        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
        # 2. Preprocess the data (e.g. torchvision.Transform).
        # 3. Return a data pair (e.g. image and label).
        pass
    def __len__(self):
        # You should change 0 to the total size of your dataset.
        return 0 

# You can then use the prebuilt data loader. 
custom_dataset = CustomDataset()
train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
                                           batch_size=64, 
                                           shuffle=True)