code: def __init__(self, root, file_path, transform=None):
      self.root = root
      self.data = pd.read_csv(file_path)
  
      self.data["disease_class"] = self.data.apply(
          lambda x: self.data.columns[x == 1], axis=1
      )
      self.data["disease_class"] = self.data["disease_class"].apply(
          lambda x: str(x[0])
      )
  
      diseases = {
          "MEL": 0,
          "NV": 1,
          "BCC": 2,
          "AKIEC": 3,
          "BKL": 4,
          "DF": 5,
          "VASC": 6,
      }
      self.data["disease_class_int"] = self.data["disease_class"].map(
          diseases
      )
  
      self.transform = transform
  
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 ISICImages object from inherited base classes: torch.utils.data with example: obj = ISICImages(root, file_path, transform)