class_docstr: Treat dense numpy array as a streamed Gensim corpus in the bag-of-words format.
  
  Notes
  -----
  No data copy is made (changes to the underlying matrix imply changes in the streamed corpus).
  
  See Also
  --------
  :func:`~gensim.matutils.corpus2dense`
      Convert Gensim corpus to dense matrix.
  :class:`~gensim.matutils.Sparse2Corpus`
      Convert sparse matrix to Gensim corpus format.
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docstr: Parameters
  ----------
  dense : numpy.ndarray
      Corpus in dense format.
  documents_columns : bool, optional
      Documents in `dense` represented as columns, as opposed to rows?
docstr_tok: ["Parameters","--","--","--","--","--","dense",":","numpy.ndarray","Corpus","in","dense","format",".","documents_columns",":","bool",",","optional","Documents","in","`","dense","`","represented","as","columns",",","as","opposed","to","rows","?"]
code: def __init__(self, dense, documents_columns=True):
      """
  
      Parameters
      ----------
      dense : numpy.ndarray
          Corpus in dense format.
      documents_columns : bool, optional
          Documents in `dense` represented as columns, as opposed to rows?
  
      """
      if documents_columns:
          self.dense = dense.T
      else:
          self.dense = dense
  
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 Dense2Corpus object  with example: obj = Dense2Corpus(dense, documents_columns)