class_docstr: Mixin for :class:`jiant.tasks.core.Task`s in the `GLUE<https://gluebenchmark.com/>`_
  benchmark.
class_docstr_tok: ["Mixin","for",":","class",":","`","jiant.tasks.core.Task","`","s","in","the","`","GLUE","<","https",":","\/\/gluebenchmark.com\/",">","`","_","benchmark","."]
docstr: Returns a tuple of (index, prediction) for GLUE benchmark submission.
  
  Args:
      pred_dict (Dict): Dictionary mapping "preds" to label ids and "guids"
      to example ids
  
  Returns:
      (tuple): tuple containing:
          indexes (List[int]): example ids
          predictions (List[str]): prediction labels (in original task
          format)
code: def get_glue_preds(cls, pred_dict: Dict):
      """Returns a tuple of (index, prediction) for GLUE benchmark submission.
  
      Args:
          pred_dict (Dict): Dictionary mapping "preds" to label ids and "guids"
          to example ids
  
      Returns:
          (tuple): tuple containing:
              indexes (List[int]): example ids
              predictions (List[str]): prediction labels (in original task
              format)
      """
      indexes = []
      predictions = []
      for pred, guid in zip(
          list(pred_dict["preds"]), list(pred_dict["guids"])
      ):
          indexes.append(int(guid.split("-")[1]))
          predictions.append(str(cls.LABELS[pred]).lower())
      return (indexes, predictions)
  
imports: import numpy
  from typing import NamedTuple, Mapping, Dict
  from enum import Enum
  from dataclasses import dataclass
  import torch
  import torch.utils.data.dataloader
  from jiant.utils.python.datastructures import ExtendedDataClassMixin, combine_dicts
label_desc: Use image node_id 1 for calling the GlueMixin obj's underlying member method code with example usage: obj.get_glue_preds(cls, pred_dict) and returns: indexes,  predictions