docstr: To ease the accessibility of data based on the class, we will use `group_examples` to group
  examples based on class. The data classes have already been mapped to numeric values and
  so are the target outputs for each training input
  
  Every key in `grouped_examples` corresponds to a class in CIFAR10 dataset. For every key in
  `grouped_examples`, every value will conform to all of the indices for the CIFAR10
  dataset examples that correspond to that key.
docstr_tok: ["To","ease","the","accessibility","of","data","based","on","the","class",",","we","will","use","`","group_examples","`","to","group","examples","based","on","class",".","The","data","classes","have","already","been","mapped","to","numeric","values","and","so","are","the","target","outputs","for","each","training","input","Every","key","in","`","grouped_examples","`","corresponds","to","a","class","in","CIFAR10","dataset",".","For","every","key","in","`","grouped_examples","`",",","every","value","will","conform","to","all","of","the","indices","for","the","CIFAR10","dataset","examples","that","correspond","to","that","key","."]
code: def group_examples(self):
      """
      To ease the accessibility of data based on the class, we will use `group_examples` to group
      examples based on class. The data classes have already been mapped to numeric values and
      so are the target outputs for each training input
  
      Every key in `grouped_examples` corresponds to a class in CIFAR10 dataset. For every key in
      `grouped_examples`, every value will conform to all of the indices for the CIFAR10
      dataset examples that correspond to that key.
      """
  
      # get the targets from CIFAR10 dataset
      np_arr = np.array(self.dataset.targets)
  
      # group examples based on class
      self.grouped_examples = {}
      for i in range(0, 10):
          self.grouped_examples[i] = np.where((np_arr == i))[0]
  
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 2 for calling the MatcherDataset obj's underlying member method code with example usage: obj.group_examples() without return types