class_docstr: ### CelebA HQ dataset
class_docstr_tok: ["#","#","#","CelebA","HQ","dataset"]
code: def __init__(self, image_size: int):
      super().__init__()
  
      # CelebA images folder
      folder = lab.get_data_path() / "celebA"
      # List of files
      self._files = [p for p in folder.glob(f"**/*.jpg")]
  
      # Transformations to resize the image and convert to tensor
      self._transform = torchvision.transforms.Compose(
          [
              torchvision.transforms.Resize(image_size),
              torchvision.transforms.ToTensor(),
          ]
      )
  
imports: from typing import List
  import torch
  import torch.utils.data
  import torchvision
  from PIL import Image
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs, option
  from labml_helpers.device import DeviceConfigs
  from labml_nn.diffusion.ddpm import DenoiseDiffusion
  from labml_nn.diffusion.ddpm.unet import UNet
label_desc: Use image node_id 1 to create a new CelebADataset object from inherited base classes: torch.utils.data with example: obj = CelebADataset(image_size)