class_docstr: Creates SQuAD dataset for Question Answering.
  Args:
      data_file (str): train.*.json eval.*.json or test.*.json.
      tokenizer (obj): Tokenizer object, e.g. AutoTokenizer.
      version_2_with_negative (bool): True if training should allow
          unanswerable questions.
      doc_stride (int): When splitting up a long document into chunks,
          how much stride to take between chunks.
      max_query_length (iny): All training files which have a duration less
          than min_duration are dropped. Can't be used if the `utt2dur` file
          does not exist. Defaults to None.
      max_seq_length (int): All training files which have a duration more
          than max_duration are dropped. Can't be used if the `utt2dur` file
          does not exist. Defaults to None.
      num_samples: number of samples you want to use for the dataset.
          If -1, use all dataset. Useful for testing.
      mode (str): Use TRAINING_MODE/EVALUATION_MODE/INFERENCE_MODE to define between
          training, evaluation and inference dataset.
      use_cache (bool): Caches preprocessed data for future usage
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code: def evaluate(
      self,
      unique_ids: List[str],
      start_logits: List[List[float]],
      end_logits: List[List[float]],
      n_best_size: int,
      max_answer_length: int,
      do_lower_case: bool,
      version_2_with_negative: bool,
      null_score_diff_threshold: float,
  ):
      (
          all_predictions,
          all_nbest_json,
          scores_diff_json,
      ) = self.get_predictions(
          unique_ids,
          start_logits,
          end_logits,
          n_best_size,
          max_answer_length,
          do_lower_case,
          version_2_with_negative,
          null_score_diff_threshold,
      )
  
      exact_match, f1 = self.evaluate_predictions(all_predictions)
  
      return exact_match, f1, all_predictions, all_nbest_json
  
imports: import collections
  import os
  import pickle
  from functools import lru_cache
  from typing import Dict, List, Optional
  import numpy
  import psutil
  import torch
  from tqdm import trange
  from nemo.collections.common.parts.utils import _compute_softmax
  from nemo.collections.nlp.data.data_utils import is_whitespace
  from nemo.collections.nlp.data.question_answering_squad.qa_squad_processing import EVALUATION_MODE, INFERENCE_MODE, TRAINING_MODE, SquadProcessor, _improve_answer_span, apply_no_ans_threshold, exact_match_score, f1_score, find_all_best_thresh, get_best_indexes, get_final_text, make_eval_dict, merge_eval, normalize_answer
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 14 for calling the SquadDataset obj's underlying member method code with example usage: obj.evaluate(unique_ids, start_logits, end_logits, n_best_size, max_answer_length, do_lower_case, version_2_with_negative, null_score_diff_threshold) and returns: exact_match,  f1,  all_predictions,  all_nbest_json