code: def __init__(
      self, batch_size=256, subset_size=256, test_batch_size=256
  ):
      trans = transforms.Compose([transforms.ToTensor()])
  
      root = "./data_fake_fmnist"
      train_set = dset.FakeData(
          image_size=(1, 28, 28), transform=transforms.ToTensor()
      )
      test_set = dset.FakeData(
          image_size=(1, 28, 28), transform=transforms.ToTensor()
      )
  
      indices = torch.randperm(len(train_set))[:subset_size]
      train_set = torch.utils.data.Subset(train_set, indices)
  
      self.train_loader = torch.utils.data.DataLoader(
          dataset=train_set, batch_size=batch_size, shuffle=True
      )
      self.test_loader = torch.utils.data.DataLoader(
          dataset=test_set, batch_size=test_batch_size, shuffle=False
      )
  
      self.name = "fakemnist"
      self.data_dims = [28, 28, 1]
      self.train_size = len(self.train_loader)
      self.test_size = len(self.test_loader)
      self.range = [0.0, 1.0]
      self.batch_size = batch_size
      self.num_training_instances = len(train_set)
      self.num_test_instances = len(test_set)
      self.likelihood_type = "gaussian"
      self.output_activation_type = "sigmoid"
  
file_path: AIX360/tests/dipvae/test_DIPVAE.py
imports: import unittest
  from aix360.algorithms.dipvae import DIPVAEExplainer
  import torch
  import torchvision.datasets
  import torchvision.transforms
  import argparse
  import numpy
label_desc: Use image node_id 1 to create a new FakeFMnistDataset object  with example: obj = FakeFMnistDataset(batch_size, subset_size, test_batch_size)