class_docstr: Remap feature ids to new values.
  
  Given a mapping between old ids and new ids (some old ids may be missing = these
  features are to be discarded), this will wrap a corpus so that iterating over
  `VocabTransform[corpus]` returns the same vectors but with the new ids.
  
  Old features that have no counterpart in the new ids are discarded. This
  can be used to filter vocabulary of a corpus "online":
  
  .. sourcecode:: pycon
  
      >>> old2new = {oldid: newid for newid, oldid in enumerate(ids_you_want_to_keep)}
      >>> vt = VocabTransform(old2new)
      >>> for vec_with_new_ids in vt[corpus_with_old_ids]:
      >>>     pass
code: def __init__(self, old2new, id2token=None):
      self.old2new = old2new
      self.id2token = id2token
  
imports: from .coherencemodel import CoherenceModel
  from .hdpmodel import HdpModel
  from .ldamodel import LdaModel
  from .lsimodel import LsiModel
  from .tfidfmodel import TfidfModel
  from .bm25model import OkapiBM25Model, LuceneBM25Model, AtireBM25Model
  from .rpmodel import RpModel
  from .logentropy_model import LogEntropyModel
  from .word2vec import Word2Vec, FAST_VERSION
  from .doc2vec import Doc2Vec
  from .keyedvectors import KeyedVectors
  from .ldamulticore import LdaMulticore
  from .phrases import Phrases
  from .normmodel import NormModel
  from .atmodel import AuthorTopicModel
  from .ldaseqmodel import LdaSeqModel
  from .fasttext import FastText
  from .translation_matrix import TranslationMatrix, BackMappingTranslationMatrix
  from .ensemblelda import EnsembleLda
  from .nmf import Nmf
  from gensim import interfaces, utils
label_desc: Use image node_id 1 to create a new VocabTransform object from inherited base classes: interfaces with example: obj = VocabTransform(old2new, id2token)