code: def __init__(
      self,
      batch_size=256,
      root_images_path="./data/ISIC2018_Task3_Training_Input/",
      file_path_labels="./data/ISIC2018_Task3_Training_GroundTruth/ISIC2018_Task3_Training_GroundTruth.csv",
  ):
      img_size = (128, 128)  # (224,224)
  
      # Transforms
      transform = transforms.Compose(
          [transforms.Resize(size=img_size), transforms.ToTensor()]
      )
  
      train_set = ISICImages(
          root=root_images_path,
          file_path=file_path_labels,
          transform=transform,
      )
  
      self.train_loader = torch.utils.data.DataLoader(
          dataset=train_set, batch_size=batch_size, shuffle=True
      )
  
      self.name = "isic"
      self.data_dims = [3, 128, 128]
      self.train_size = len(self.train_loader)
      self.range = [-1.0, 1.0]
      self.batch_size = batch_size
      self.num_training_instances = len(train_set)
      self.likelihood_type = "gaussian"
      self.output_activation_type = "tanh"
  
imports: import torch
  from torchvision import transforms
  import torch.nn
  import pandas
  from PIL import Image
  import numpy
  import math
  import os
  import matplotlib.pyplot
  from aix360.algorithms.dipvae.dipvae_utils import VAE, DIPVAE
  from aix360.algorithms.dipvae import DIPVAEExplainer
  from aix360.algorithms.dipvae.dipvae_utils import plot_reconstructions, plot_latent_traversal
label_desc: Use image node_id 1 to create a new ISICDataset object  with example: obj = ISICDataset(batch_size, root_images_path, file_path_labels)