class_docstr: Convert a sequence of dense/sparse vectors into a streamed Gensim corpus object.
  
  See Also
  --------
  :func:`~gensim.matutils.corpus2csc`
      Convert corpus in Gensim format to `scipy.sparse.csc` matrix.
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docstr: Parameters
  ----------
  vecs : iterable of {`numpy.ndarray`, `scipy.sparse`}
      Input vectors.
docstr_tok: ["Parameters","--","--","--","--","--","vecs",":","iterable","of","{","`","numpy.ndarray","`",",","`","scipy.sparse","`","}","Input","vectors","."]
code: def __init__(self, vecs):
      """
  
      Parameters
      ----------
      vecs : iterable of {`numpy.ndarray`, `scipy.sparse`}
          Input vectors.
  
      """
      self.vecs = vecs
  
imports: from __future__ import with_statement
  import logging
  import math
  from gensim import utils
  import numpy
  import scipy.sparse
  from scipy.stats import entropy
  from scipy.linalg import get_blas_funcs, triu
  from scipy.linalg.lapack import get_lapack_funcs
  from scipy.special import psi
label_desc: Use image node_id 1 to create a new Scipy2Corpus object  with example: obj = Scipy2Corpus(vecs)