class_docstr: Question Answering in Context (QuAC) is a dataset for modeling, understanding, and  participating in information seeking dialog.
class_docstr_tok: ["Question","Answering","in","Context","(","QuAC",")","is","a","dataset","for","modeling",",","understanding",",","and","participating","in","information","seeking","dialog","."]
code: def _split_generators(self, dl_manager):
      urls = {
          "train": _URLS["train"],
          "validation": _URLS["validation"],
      }
      data_dir = dl_manager.download_and_extract(urls)
      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={
                  "filepath": data_dir["train"],
                  "split": "train",
              },
          ),
          datasets.SplitGenerator(
              name=datasets.Split.VALIDATION,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={
                  "filepath": data_dir["validation"],
                  "split": "validation",
              },
          ),
      ]
  
imports: import json
  import datasets
label_desc: Use image node_id 2 for calling the Quac obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list