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__(
      self,
      *,
      ngrams: Optional[int | Collection[int] | DocLikeToSpans] = None,
      entities: Optional[bool | DocLikeToSpans] = None,
      noun_chunks: Optional[bool | DocLikeToSpans] = None,
      normalize: Optional[str | Callable[[Span], str]] = None,
      dedupe: bool = True,
  ):
      self.tokenizers = self._init_tokenizers(
          ngrams, entities, noun_chunks
      )
      self.normalize = self._init_normalize(normalize)
      self.dedupe = dedupe
  
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 1 to create a new TermsTokenizer object  with example: obj = TermsTokenizer()