class_docstr: Transform each ``Doc`` or ``Span`` in a sequence thereof into a sequence of terms
  as strings, where "terms" are the concatenated combination of constituent
  n-grams, entities, and/or noun chunks.
  
  Args:
      ngrams
      entities
      noun_chunks
      normalize
      dedupe
class_docstr_tok: ["Transform","each","``","Doc","``","or","``","Span","``","in","a","sequence","thereof","into","a","sequence","of","terms","as","strings",",","where","``","terms","''","are","the","concatenated","combination","of","constituent","n-grams",",","entities",",","and\/or","noun","chunks",".","Args",":","ngrams","entities","noun_chunks","normalize","dedupe"]
code: def _init_tokenizers(
      self, ngrams, entities, noun_chunks
  ) -> tuple[DocLikeToSpans, ...]:
      ngs_tokenizer = self._init_ngrams_tokenizer(ngrams)
      ents_tokenizer = self._init_entities_tokenizer(entities)
      ncs_tokenizer = self._init_noun_chunks_tokenizer(noun_chunks)
      tokenizers = tuple(
          tokenizer
          for tokenizer in [
              ngs_tokenizer,
              ents_tokenizer,
              ncs_tokenizer,
          ]
          if tokenizer is not None
      )
      if not tokenizers:
          raise ValueError(
              "at least one term tokenizer must be specified"
          )
      else:
          return tokenizers
  
file_path: textacy/src/textacy/tokenizers/terms.py
imports: from __future__ import annotations
  import operator
  from functools import partial
  from typing import Callable, Collection, Iterable, Optional
  from cytoolz import itertoolz
  from spacy.tokens import Span
  from ..None import extract, types
label_desc: Use image node_id 3 for calling the TermsTokenizer obj's underlying member method code with example usage: obj._init_tokenizers(ngrams, entities, noun_chunks) and returns: tokenizers