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_normalize(
      self, normalize: Optional[str | Callable[[Span], str]]
  ) -> Callable[[Span], str]:
      if not normalize:
          return operator.attrgetter("text")
      elif normalize == "lower":
          return lambda span: span.text.lower()
      elif normalize == "lemma":
          return lambda span: span.lemma_
      elif callable(normalize):
          return normalize
      else:
          raise ValueError()
  
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 7 for calling the TermsTokenizer obj's underlying member method code with example usage: obj._init_normalize(normalize) and returns: operator, function, function, normalize