class_docstr: SROIE dataset from `"ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction"
  <https://arxiv.org/pdf/2103.10213.pdf>`_.
  
  .. image:: https://doctr-static.mindee.com/models?id=v0.5.0/sroie-grid.png&src=0
      :align: center
  
  >>> from doctr.datasets import SROIE
  >>> train_set = SROIE(train=True, download=True)
  >>> img, target = train_set[0]
  
  Args:
  ----
      train: whether the subset should be the training one
      use_polygons: whether polygons should be considered as rotated bounding box (instead of straight ones)
      recognition_task: whether the dataset should be used for recognition task
      **kwargs: keyword arguments from `VisionDataset`.
code: def extra_repr(self) -> str:
      return f"train={self.train}"
  
file_path: doctr/doctr/datasets/sroie.py
imports: import csv
  import os
  from pathlib import Path
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  from tqdm import tqdm
  from .datasets import VisionDataset
  from .utils import convert_target_to_relative, crop_bboxes_from_image
label_desc: Use image node_id 2 for calling the SROIE obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self