class_docstr: A `Tokenizer` that uses spaCy's tokenizer.  It's fast and reasonable - this is the
  recommended `Tokenizer`. By default it will return allennlp Tokens,
  which are small, efficient NamedTuples (and are serializable). If you want
  to keep the original spaCy tokens, pass keep_spacy_tokens=True.  Note that we leave one particular piece of
  post-processing for later: the decision of whether or not to lowercase the token.  This is for
  two reasons: (1) if you want to make two different casing decisions for whatever reason, you
  won't have to run the tokenizer twice, and more importantly (2) if you want to lowercase words
  for your word embedding, but retain capitalization in a character-level representation, we need
  to retain the capitalization here.
  
  Registered as a `Tokenizer` with name "spacy", which is currently the default.
  
  # Parameters
  
  language : `str`, optional, (default=`"en_core_web_sm"`)
      Spacy model name.
  pos_tags : `bool`, optional, (default=`False`)
      If `True`, performs POS tagging with spacy model on the tokens.
      Generally used in conjunction with :class:`~allennlp.data.token_indexers.pos_tag_indexer.PosTagIndexer`.
  parse : `bool`, optional, (default=`False`)
      If `True`, performs dependency parsing with spacy model on the tokens.
      Generally used in conjunction with :class:`~allennlp.data.token_indexers.pos_tag_indexer.DepLabelIndexer`.
  ner : `bool`, optional, (default=`False`)
      If `True`, performs dependency parsing with spacy model on the tokens.
      Generally used in conjunction with :class:`~allennlp.data.token_indexers.ner_tag_indexer.NerTagIndexer`.
  keep_spacy_tokens : `bool`, optional, (default=`False`)
      If `True`, will preserve spacy token objects, We copy spacy tokens into our own class by default instead
      because spacy Cython Tokens can't be pickled.
  split_on_spaces : `bool`, optional, (default=`False`)
      If `True`, will split by spaces without performing tokenization.
      Used when your data is already tokenized, but you want to perform pos, ner or parsing on the tokens.
  start_tokens : `Optional[List[str]]`, optional, (default=`None`)
      If given, these tokens will be added to the beginning of every string we tokenize.
  end_tokens : `Optional[List[str]]`, optional, (default=`None`)
      If given, these tokens will be added to the end of every string we tokenize.
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code: def tokenize(self, text: str) -> List[Token]:
      # This works because our Token class matches spacy's.
      return self._sanitize(_remove_spaces(self.spacy(text)))
  
file_path: allennlp/allennlp/data/tokenizers/spacy_tokenizer.py
imports: from typing import List, Optional
  import spacy
  from spacy.tokens import Doc
  from allennlp.common.util import get_spacy_model
  from allennlp.data.tokenizers.token_class import Token
  from allennlp.data.tokenizers.tokenizer import Tokenizer
label_desc: Use image node_id 4 for calling the SpacyTokenizer obj's underlying member method code with example usage: obj.tokenize(text) and returns: self