docstr: Process the data to the following format.
  
  Note that we mask all the special tokens except the CLS token. The reason for not masking
  the CLS token is that if the question is not answerable, we will set the start and end to
  be 0.
  
  
  Merged:      <CLS> Question <SEP> Context <SEP>
  Segment IDs:  0       0       0      1      1
  Mask:         0       1       1      0      1
  
  Here, we need to emphasize that when mask = 1, the data are actually not masked!
  
  Parameters
  ----------
  feature
      Tokenized SQuAD feature
  
  Returns
  -------
  ret
      Divide the feature into multiple chunks and extract the feature which contains
      the following:
      - data
          The data that concatenates the query and the context + special tokens
      - valid_length
          The valid_length of the data
      - segment_ids
          We assign the query part as segment 0 and the context part as segment 1.
      - masks
          We mask all the special tokens. 1 --> not masked, 0 --> masked.
      - is_impossible
          Whether the provided context is impossible to answer or not.
      - gt_start
          The ground-truth start location of the span
      - gt_end
          The ground-truth end location of the span
      - chunk_start
          The start of the chunk
      - chunk_length
          The length of the chunk
code: def process_sample(self, feature: SquadFeature):
      """Process the data to the following format.
  
      Note that we mask all the special tokens except the CLS token. The reason for not masking
      the CLS token is that if the question is not answerable, we will set the start and end to
      be 0.
  
  
      Merged:      <CLS> Question <SEP> Context <SEP>
      Segment IDs:  0       0       0      1      1
      Mask:         0       1       1      0      1
  
      Here, we need to emphasize that when mask = 1, the data are actually not masked!
  
      Parameters
      ----------
      feature
          Tokenized SQuAD feature
  
      Returns
      -------
      ret
          Divide the feature into multiple chunks and extract the feature which contains
          the following:
          - data
              The data that concatenates the query and the context + special tokens
          - valid_length
              The valid_length of the data
          - segment_ids
              We assign the query part as segment 0 and the context part as segment 1.
          - masks
              We mask all the special tokens. 1 --> not masked, 0 --> masked.
          - is_impossible
              Whether the provided context is impossible to answer or not.
          - gt_start
              The ground-truth start location of the span
          - gt_end
              The ground-truth end location of the span
          - chunk_start
              The start of the chunk
          - chunk_length
              The length of the chunk
      """
      ret = []
      truncated_query_ids = feature.query_token_ids[
          : self._max_query_length
      ]
      chunks = feature.get_chunks(
          doc_stride=self._doc_stride,
          max_chunk_length=self._max_seq_length
          - len(truncated_query_ids)
          - 3,
      )
      for chunk in chunks:
          data = _np.array(
              [self.cls_id]
              + truncated_query_ids
              + [self.sep_id]
              + feature.context_token_ids[
                  chunk.start : (chunk.start + chunk.length)
              ]
              + [self.sep_id],
              dtype=_np.int32,
          )
          valid_length = len(data)
          segment_ids = _np.array(
              [0]
              + [0] * len(truncated_query_ids)
              + [0]
              + [1] * chunk.length
              + [1],
              dtype=_np.int32,
          )
          masks = _np.array(
              [0]
              + [1] * len(truncated_query_ids)
              + [1]
              + [0] * chunk.length
              + [1],
              dtype=_np.int32,
          )
          context_offset = len(truncated_query_ids) + 2
          if chunk.gt_start_pos is None and chunk.gt_end_pos is None:
              start_pos = 0
              end_pos = 0
          else:
              # Here, we increase the start and end because we put query before context
              start_pos = chunk.gt_start_pos + context_offset
              end_pos = chunk.gt_end_pos + context_offset
          chunk_feature = self.ChunkFeature(
              qas_id=feature.qas_id,
              data=data,
              valid_length=valid_length,
              segment_ids=segment_ids,
              masks=masks,
              is_impossible=chunk.is_impossible,
              gt_start=start_pos,
              gt_end=end_pos,
              context_offset=context_offset,
              chunk_start=chunk.start,
              chunk_length=chunk.length,
          )
          ret.append(chunk_feature)
      return ret
  
imports: import numpy
  import gluonnlp.data.batchify
  import collections
  import dataclasses
  import mxnet
  from mxnet import np, npx
  from mxnet.gluon import nn
  from mxnet.gluon.nn import HybridBlock
  from dataclasses import dataclass
  from tqdm import tqdm
  from typing import Optional, List, Tuple
  from gluonnlp.data.tokenizers import BaseTokenizerWithVocab
  from gluonnlp.utils.preprocessing import match_tokens_with_char_spans
  from gluonnlp.layers import get_activation
  from gluonnlp.op import select_vectors_by_position
  from gluonnlp.attention_cell import masked_logsoftmax, masked_softmax
  import string
  import re
  import json
label_desc: Use image node_id 2 for calling the SquadDatasetProcessor obj's underlying member method code with example usage: obj.process_sample(feature) and returns: ret