code: def __init__(self, root, train, download=False):
      super(MatcherDataset, self).__init__()
  
      # get CIFAR10 dataset
      self.dataset = datasets.CIFAR10(
          root, train=train, download=download
      )
  
      # convert data from numpy array to Tensor
      self.data = torch.from_numpy(self.dataset.data)
  
      # shift the dimensions of dataset to match the initial input layer dimensions
      self.data = torch.movedim(self.data, (0, 1, 2, 3), (0, 2, 3, 1))
  
      # convert targets list to torch Tensor
      self.dataset.targets = torch.tensor(self.dataset.targets)
  
      self.group_examples()
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision import datasets
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Engine, Events
  from ignite.handlers.param_scheduler import LRScheduler
  from ignite.metrics import Accuracy, RunningAverage
  from ignite.utils import manual_seed
label_desc: Use image node_id 1 to create a new MatcherDataset object from inherited base classes: Dataset with example: obj = MatcherDataset(root, train, download)