class_docstr: Derive a specific sub-class of a VAE for SVHN 
class_docstr_tok: ["Derive","a","specific","sub-class","of","a","VAE","for","SVHN"]
code: def getDataLoaders(batch_size, shuffle=True, device="cuda"):
      kwargs = (
          {"num_workers": 1, "pin_memory": True}
          if device == "cuda"
          else {}
      )
      tx = transforms.ToTensor()
      train = DataLoader(
          datasets.SVHN(
              "../data", split="train", download=True, transform=tx
          ),
          batch_size=batch_size,
          shuffle=shuffle,
          **kwargs,
      )
      test = DataLoader(
          datasets.SVHN(
              "../data", split="test", download=True, transform=tx
          ),
          batch_size=batch_size,
          shuffle=shuffle,
          **kwargs,
      )
      return train, test
  
imports: import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  from numpy import sqrt
  from torch.utils.data import DataLoader
  from torchvision import transforms, datasets
  from torchvision.utils import save_image, make_grid
  from utils import Constants
  from vis import plot_embeddings, plot_kls_df
  from .vae import VAE
label_desc: Use image node_id 3 for calling the SVHN obj's underlying member method code with example usage: obj.getDataLoaders(batch_size, shuffle, device) and returns: train,  test