class_docstr: **RTE** 数据集的 **Loader**，如果您使用了该数据，请引用 **GLUE Benchmark** ：
  
      https://openreview.net/pdf?id=rJ4km2R5t7
      
  读取数据的格式为：第一行为标题（具体内容会被忽略），之后每一行是一个 sample，由 **index** 、**句子1** 、**句子2** 和 **标签** 
  构成（以制表符分割），数据结构如下::
  
      index   sentence1       sentence2       label
      0       Dana Reeve, the widow of the actor Christopher Reeve, has died of lung cancer at age 44, according to the Christopher Reeve Foundation. Christopher Reeve had an accident.      not_entailment
  
  读取的 :class:`~fastNLP.core.DataSet` 将具备以下的内容：``raw_words1`` 是 ``sentence1`` ， ``raw_words2`` 是 ``sentence2`` ， ``target`` 是 ``label`` 。
  测试集中没有 ``target`` 列。
  
  .. csv-table::
      :header: "raw_words1", "raw_words2", "target"
  
      "Dana Reeve, the widow of the actor...", "Christopher Reeve had an...", "not_entailment"
      "...","..."
code: def _load(self, path: str):
      ds = DataSet()
  
      with open(path, "r", encoding="utf-8") as f:
          f.readline()  # 跳过header
          if path.endswith("test.tsv"):
              logger.warning("RTE's test file has no target.")
              for line in f:
                  line = line.strip()
                  if line:
                      parts = line.split("\t")
                      raw_words1 = parts[1]
                      raw_words2 = parts[2]
                      if raw_words1 and raw_words2:
                          ds.append(
                              Instance(
                                  raw_words1=raw_words1,
                                  raw_words2=raw_words2,
                              )
                          )
          else:
              for line in f:
                  line = line.strip()
                  if line:
                      parts = line.split("\t")
                      raw_words1 = parts[1]
                      raw_words2 = parts[2]
                      target = parts[-1]
                      if raw_words1 and raw_words2 and target:
                          ds.append(
                              Instance(
                                  raw_words1=raw_words1,
                                  raw_words2=raw_words2,
                                  target=target,
                              )
                          )
      return ds
  
imports: import os
  from typing import Union, Dict
  from .csv import CSVLoader
  from .json import JsonLoader
  from .loader import Loader
  from fastNLP.io.data_bundle import DataBundle
  from ..utils import check_loader_paths
  from fastNLP.core.dataset import DataSet, Instance
  from fastNLP.core.log import logger
label_desc: Use image node_id 2 for calling the RTELoader obj's underlying member method code with example usage: obj._load(path) and returns: ds