class_docstr: Corpus serialized using the `sparse coordinate Matrix Market format
  <https://math.nist.gov/MatrixMarket/formats.html>`_.
  
  Wrap a term-document matrix on disk (in matrix-market format), and present it
  as an object which supports iteration over the matrix rows (~documents).
  
  Notes
  -----
  The file is read into memory one document at a time, not the whole matrix at once,
  unlike e.g. `scipy.io.mmread` and other implementations. This allows you to **process corpora which are larger
  than the available RAM**, in a streamed manner.
  
  Example
  --------
  .. sourcecode:: pycon
  
      >>> from gensim.corpora.mmcorpus import MmCorpus
      >>> from gensim.test.utils import datapath
      >>>
      >>> corpus = MmCorpus(datapath('test_mmcorpus_with_index.mm'))
      >>> for document in corpus:
      ...     pass
docstr: Parameters
  ----------
  fname : {str, file-like object}
      Path to file in MM format or a file-like object that supports `seek()`
      (e.g. a compressed file opened by `smart_open <https://github.com/RaRe-Technologies/smart_open>`_).
code: def __init__(self, fname):
      """
  
      Parameters
      ----------
      fname : {str, file-like object}
          Path to file in MM format or a file-like object that supports `seek()`
          (e.g. a compressed file opened by `smart_open <https://github.com/RaRe-Technologies/smart_open>`_).
  
      """
      # avoid calling super(), too confusing
      IndexedCorpus.__init__(self, fname)
      matutils.MmReader.__init__(self, fname)
  
file_path: gensim/gensim/corpora/mmcorpus.py
imports: import logging
  from gensim import matutils
  from gensim.corpora import IndexedCorpus
label_desc: Use image node_id 1 to create a new MmCorpus object from inherited base classes: matutils, IndexedCorpus with example: obj = MmCorpus(fname)