docstr: Given a short name for the dataset, return a map that has descriptor for what
  the data from the dataset is, plus the examples (useful since synthetic data has
  different folders for Perturbations)
docstr_tok: ["Given","a","short","name","for","the","dataset",",","return","a","map","that","has","descriptor","for","what","the","data","from","the","dataset","is",",","plus","the","examples","(","useful","since","synthetic","data","has","different","folders","for","Perturbations",")"]
code: def load_data(cls, dataset) -> Dict[str, List[Example]]:
      """
      Given a short name for the dataset, return a map that has descriptor for what
      the data from the dataset is, plus the examples (useful since synthetic data has
      different folders for Perturbations)
      """
      if dataset == "test":
          return {"test": [Example(x, x, x + 2) for x in range(8)]}
      if dataset in INPUT_DATA_HUMAN:
          return {dataset: cls.load_human(dataset)}
      assert dataset in INPUT_DATA_SYNTHETIC
      return cls.load_synthetic_datas(dataset)
  
file_path: ParlAI/projects/roscoe/baselines/run.py
imports: import argparse
  import glob
  import os
  from typing import Dict, List
  import pandas
  import json
  from projects.roscoe.utils import split_gsm8k_gpt3_generations_to_steps
  from projects.roscoe.synthetic_evaluation.synthetic_roscoe import SyntheticChain
  from projects.roscoe.baselines.constants import Example, UseRef, BASELINE_SCORES, INPUT_DATA_SYNTHETIC, INPUT_DATA_FILES_HUMAN, INPUT_DATA_HUMAN, ALL_DATASETS, PERTURBATIONS
  from projects.roscoe.baselines.scores import SCORES_TO_CLASS
  from nltk import sent_tokenize
label_desc: Use image node_id 3 for calling the BaselineDataLoader obj's underlying member method code with example usage: obj.load_data(cls, dataset) and returns: cls, dict, dict