class_docstr: USPS Dataset.
  
  Args:
      root (string): Root directory of dataset where dataset file exist.
      train (bool, optional): If True, resample from dataset randomly.
      download (bool, optional): If true, downloads the dataset
          from the internet and puts it in root directory.
          If dataset is already downloaded, it is not downloaded again.
      transform (callable, optional): A function/transform that takes in
          an PIL image and returns a transformed version.
          E.g, ``transforms.RandomCrop``
docstr: Get images and target for data loader.
  Args:
      index (int): Index
  Returns:
      tuple: (image, target) where target is index of the target class.
docstr_tok: ["Get","images","and","target","for","data","loader",".","Args",":","index","(","int",")",":","Index","Returns",":","tuple",":","(","image",",","target",")","where","target","is","index","of","the","target","class","."]
code: def __getitem__(self, index):
      """Get images and target for data loader.
      Args:
          index (int): Index
      Returns:
          tuple: (image, target) where target is index of the target class.
      """
      img, label = self.data[index, ::], self.targets[index]
      if self.transform is not None:
          img = self.transform(img)
      label = torch.LongTensor([np.int64(label).item()])
      # label = torch.FloatTensor([label.item()])
      return img, label
  
file_path: pykale/kale/loaddata/usps.py
imports: import gzip
  import logging
  import os
  import pickle
  import urllib
  import numpy
  import torch
  import torch.utils.data
label_desc: Use image node_id 2 for calling the USPS obj's underlying member method code with example usage: obj.__getitem__(index) and returns: img,  label