code: def __call__(self):
      download()
      dataset = load_files(self.path, categories=["pos", "neg"])
      X, y = dataset["data"], dataset["target"]
      X = np.asarray([x.decode() for x in X])  # decode from bytes
      return X, y
  
imports: import os
  from urllib.request import urlretrieve
  import tarfile
  from dstoolbox.transformers import Padder2d
  from dstoolbox.transformers import TextFeaturizer
  import numpy
  from sklearn.datasets import load_files
  from sklearn.pipeline import Pipeline
  from skorch import NeuralNetClassifier
  import torch
  from torch import nn
  from palladium.interfaces import DatasetLoader
label_desc: Use image node_id 2 for calling the DatasetLoader obj's underlying member method code with example usage: obj.__call__() and returns: X,  y