class_docstr: Basic tokenizer that tokenizes the input via white spaces.
class_docstr_tok: ["Basic","tokenizer","that","tokenizes","the","input","via","white","spaces","."]
code: def decode(self, tokens):
      is_multiple_sentences = is_tokens_from_multiple_sentences(tokens)
      if not is_multiple_sentences:
          tokens = [tokens]
      token_type = get_token_type(tokens)
      if token_type is str:
          ret = [" ".join(ele_tokens) for ele_tokens in tokens]
      elif token_type is int:
          if self._vocab is None:
              raise TokenizerDecodeWithoutVocabError
          ret = [
              " ".join(self.vocab.to_tokens(ele_tokens))
              for ele_tokens in tokens
          ]
      else:
          raise TokenTypeNotSupportedError(token_type)
      if is_multiple_sentences:
          return ret
      else:
          return ret[0]
  
file_path: gluon-nlp/src/gluonnlp/data/tokenizers/whitespace.py
imports: from typing import Optional
  from .base import TOKENIZER_REGISTRY, BaseTokenizerWithVocab, TokenizerEncodeWithoutVocabError, TokenizerDecodeWithoutVocabError, TokenTypeNotSupportedError, rebuild_offset_from_tokens, is_tokens_from_multiple_sentences, get_token_type
  from ..vocab import Vocab
label_desc: Use image node_id 4 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj.decode(tokens) and returns: ret, ret