class_docstr: Transformation interface.
  
  A 'transformation' is any object which accepts document in BoW format via the `__getitem__` (notation `[]`)
  and returns another sparse document in its stead:
  
  .. sourcecode:: pycon
  
      >>> from gensim.models import LsiModel
      >>> from gensim.test.utils import common_dictionary, common_corpus
      >>>
      >>> model = LsiModel(common_corpus, id2word=common_dictionary)
      >>> bow_vector = model[common_corpus[0]]  # model applied through __getitem__ on one document from corpus.
      >>> bow_corpus = model[common_corpus]  # also, we can apply model on the full corpus
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docstr: Transform a single document, or a whole corpus, from one vector space into another.
  
  Parameters
  ----------
  vec : {list of (int, number), iterable of list of (int, number)}
      Document in bag-of-words, or streamed corpus.
docstr_tok: ["Transform","a","single","document",",","or","a","whole","corpus",",","from","one","vector","space","into","another",".","Parameters","--","--","--","--","--","vec",":","{","list","of","(","int",",","number",")",",","iterable","of","list","of","(","int",",","number",")","}","Document","in","bag-of-words",",","or","streamed","corpus","."]
code: def __getitem__(self, vec):
      """Transform a single document, or a whole corpus, from one vector space into another.
  
      Parameters
      ----------
      vec : {list of (int, number), iterable of list of (int, number)}
          Document in bag-of-words, or streamed corpus.
  
      """
      raise NotImplementedError(
          "cannot instantiate abstract base class"
      )
  
imports: import logging
  from gensim import utils, matutils
label_desc: Use image node_id 1 for calling the TransformationABC obj's underlying member method code with example usage: obj.__getitem__(vec) without return types