class_docstr: Use Mnist Dataset for LR training,
  small digits (0-4) and large digits (5-9) are calssified as two class
class_docstr_tok: ["Use","Mnist","Dataset","for","LR","training",",","small","digits","(","0-4",")","and","large","digits","(","5-9",")","are","calssified","as","two","class"]
code: def __init__(self, batch_size=32, train=True) -> None:
      super().__init__(batch_size, train)
      self.num_classes = 2
  
file_path: tf-encrypted/examples/benchmark/training/lr_mnist_dataset.py
imports: import tensorflow
  from tf_encrypted.keras.datasets import MnistDataset
  from tf_encrypted.keras.datasets.convert import decode_data
  from tf_encrypted.keras.datasets.convert import decode_image
  from tf_encrypted.keras.datasets.convert import decode_label
label_desc: Use image node_id 1 to create a new LRMnistDataset object from inherited base classes: MnistDataset with example: obj = LRMnistDataset(batch_size, train)