class_docstr: MJSynth dataset from `"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition"
  <https://www.robots.ox.ac.uk/~vgg/data/text/>`_.
  
  >>> # NOTE: This is a pure recognition dataset without bounding box labels.
  >>> # NOTE: You need to download the dataset.
  >>> from doctr.datasets import MJSynth
  >>> train_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
  >>>                     label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt",
  >>>                     train=True)
  >>> img, target = train_set[0]
  >>> test_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
  >>>                    label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt")
  >>>                    train=False)
  >>> img, target = test_set[0]
  
  Args:
  ----
      img_folder: folder with all the images of the dataset
      label_path: path to the file with the labels
      train: whether the subset should be the training one
      **kwargs: keyword arguments from `AbstractDataset`.
code: def extra_repr(self) -> str:
      return f"train={self.train}"
  
file_path: doctr/doctr/datasets/mjsynth.py
imports: import os
  from typing import Any, List, Tuple
  from tqdm import tqdm
  from .datasets import AbstractDataset
label_desc: Use image node_id 2 for calling the MJSynth obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self