code: def sparse2full(doc, length):
      """Convert a document in Gensim bag-of-words format into a dense numpy array.
  
      Parameters
      ----------
      doc : list of (int, number)
          Document in BoW format.
      length : int
          Vector dimensionality. This cannot be inferred from the BoW, and you must supply it explicitly.
          This is typically the vocabulary size or number of topics, depending on how you created `doc`.
  
      Returns
      -------
      numpy.ndarray
          Dense numpy vector for `doc`.
  
      See Also
      --------
      :func:`~gensim.matutils.full2sparse`
          Convert dense array to gensim bag-of-words format.
  
      """
      result = np.zeros(
          length, dtype=np.float32
      )  # fill with zeroes (default value)
      # convert indices to int as numpy 1.12 no longer indexes by floats
      doc = ((int(id_), float(val_)) for (id_, val_) in doc)
  
      doc = dict(doc)
      # overwrite some of the zeroes with explicit values
      result[list(doc)] = list(doc.values())
      return result
  
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 10 for calling a global function with example usage: sparse2full(doc, length) and returns: result