class_docstr: Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
  
  Args:
      unique_id: The unique identifier corresponding to that example.
      start_logits: The logits corresponding to the start of the answer
      end_logits: The logits corresponding to the end of the answer
class_docstr_tok: ["Constructs","a","SquadResult","which","can","be","used","to","evaluate","a","model","'s","output","on","the","SQuAD","dataset",".","Args",":","unique_id",":","The","unique","identifier","corresponding","to","that","example",".","start_logits",":","The","logits","corresponding","to","the","start","of","the","answer","end_logits",":","The","logits","corresponding","to","the","end","of","the","answer"]
code: def __init__(
      self,
      unique_id,
      start_logits,
      end_logits,
      start_top_index=None,
      end_top_index=None,
      cls_logits=None,
  ):
      self.start_logits = start_logits
      self.end_logits = end_logits
      self.unique_id = unique_id
  
      if start_top_index:
          self.start_top_index = start_top_index
          self.end_top_index = end_top_index
          self.cls_logits = cls_logits
  
imports: import json
  import os
  from functools import partial
  from multiprocessing import Pool, cpu_count
  import numpy
  from tqdm import tqdm
  from ...models.bert.tokenization_bert import whitespace_tokenize
  from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
  from ...utils import is_tf_available, is_torch_available, logging
  from .utils import DataProcessor
label_desc: Use image node_id 1 to create a new SquadResult object  with example: obj = SquadResult(unique_id, start_logits, end_logits, start_top_index, end_top_index, cls_logits)