class_docstr: This masks out tokens according to the given tokenizer.
  
  The masked variables are
  
  output_type : "string" (default) or "token_ids"
code: def __call__(self, mask, s):
      mask = self._standardize_mask(mask, s)
      self._update_s_cache(s)
  
      # if we have a fixed prefix or suffix then we need to grow the mask to account for that
      if self.keep_prefix > 0 or self.keep_suffix > 0:
          mask = mask.copy()
          mask[: self.keep_prefix] = True
          mask[-self.keep_suffix :] = True
  
      if self.output_type == "string":
          # if self.mask_token == "":
          #     out = self._segments_s[mask]
          # else:
          #     #out = np.array([self._segments_s[i] if mask[i] else self.mask_token for i in range(len(mask))])
          out_parts = []
          is_previous_appended_token_mask_token = False
          sep_token = getattr_silent(self.tokenizer, "sep_token")
          for i, v in enumerate(mask):
              # mask ignores separator tokens and keeps them unmasked
              if v or sep_token == self._segments_s[i]:
                  out_parts.append(self._segments_s[i])
                  is_previous_appended_token_mask_token = False
              else:
                  if not self.collapse_mask_token or (
                      self.collapse_mask_token
                      and not is_previous_appended_token_mask_token
                  ):
                      out_parts.append(" " + self.mask_token)
                      is_previous_appended_token_mask_token = True
          out = "".join(out_parts)
  
          # tokenizers which treat spaces like parts of the tokens and dont replace the special token while decoding need further postprocessing
          # by replacing whitespace encoded as '_' for sentencepiece tokenizer or 'Ġ' for sentencepiece like encoding (GPT2TokenizerFast)
          # with ' '
          if safe_isinstance(self.tokenizer, SENTENCEPIECE_TOKENIZERS):
              out = out.replace("▁", " ")
  
          # replace sequence of spaces with a single space and strip beginning and end spaces
          out = re.sub(
              r"[\s]+", " ", out
          ).strip()  # TODOmaybe: should do strip?? (originally because of fast vs. slow tokenizer differences)
  
      else:
          if self.mask_token_id is None:
              out = self._tokenized_s[mask]
          else:
              out = np.array(
                  [
                      self._tokenized_s[i]
                      if mask[i]
                      else self.mask_token_id
                      for i in range(len(mask))
                  ]
              )
              # print("mask len", len(out))
              # # crop the output if needed
              # if self.max_length is not None and len(out) > self.max_length:
              #     new_out = np.zeros(self.max_length)
              #     new_out[:] = out[:self.max_length]
              #     new_out[-self.keep_suffix:] = out[-self.keep_suffix:]
              #     out = new_out
  
      # for some sentences with strange configurations around the separator tokens, tokenizer encoding/decoding may contain
      # extra unnecessary tokens, for example ''. you may want to strip out spaces adjacent to separator tokens. Refer to PR
      # for more details.
      return (np.array([out]),)
  
imports: import math
  import re
  import numpy
  from .._serializable import Deserializer, Serializer
  from ..utils import safe_isinstance
  from ..utils.transformers import SENTENCEPIECE_TOKENIZERS, getattr_silent, parse_prefix_suffix_for_tokenizer
  from ._masker import Masker
label_desc: Use image node_id 2 for calling the Text obj's underlying member method code with example usage: obj.__call__(mask, s) and returns: np