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"]
docstr: Convert a sequence of spaCy Docs or Spans into an ordered, nested sequence
  of terms as strings.
  
  Args:
      doclikes
  
  Yields:
      Ordered sequence of terms as strings for next Doc or Span.
docstr_tok: ["Convert","a","sequence","of","spaCy","Docs","or","Spans","into","an","ordered",",","nested","sequence","of","terms","as","strings",".","Args",":","doclikes","Yields",":","Ordered","sequence","of","terms","as","strings","for","next","Doc","or","Span","."]
code: def transform(
      self, doclikes: Iterable[types.DocLike]
  ) -> Iterable[tuple[str, ...]]:
      """
      Convert a sequence of spaCy Docs or Spans into an ordered, nested sequence
      of terms as strings.
  
      Args:
          doclikes
  
      Yields:
          Ordered sequence of terms as strings for next Doc or Span.
      """
      normalize_ = self.normalize
      for doclike in doclikes:
          terms = itertoolz.concat(
              tokenizer(doclike) for tokenizer in self.tokenizers
          )
          if self.dedupe is True:
              terms = itertoolz.unique(
                  terms, lambda span: (span.start, span.end)
              )
          yield tuple(normalize_(term) for term in terms)
  
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 9 for calling the TermsTokenizer obj's underlying member method code with example usage: obj.transform(doclikes) without return types