class_docstr: An implementation of `"BoostNE" <https://arxiv.org/abs/1808.08627>`_
  from the ASONAM '19 paper "Multi-Level Network Embedding with Boosted Low-Rank
  Matrix Approximation". The procedure uses non-negative matrix factorization
  iteratively to decompose the residuals obtained by previous factorization models.
  The base target matrix is a pooled sum of adjacency matrix powers.
  
  Args:
      dimensions (int): Number of individual embedding dimensions. Default is 8.
      iterations (int): Number of boosting iterations. Default is 16.
      order (int): Number of adjacency matrix powers. Default is 2.
      alpha (float): NMF regularization parameter. Default is 0.01.
      seed (int): Random seed value. Default is 42.
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docstr: Getting the node embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of nodes.
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code: def get_embedding(self) -> np.array:
      r"""Getting the node embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of nodes.
      """
      embedding = np.concatenate(self._embeddings, axis=1)
      return embedding
  
imports: import numpy
  import networkx
  from scipy import sparse
  from sklearn.decomposition import NMF
  from karateclub.estimator import Estimator
  from inspect import signature
label_desc: Use image node_id 12 for calling the BoostNE obj's underlying member method code with example usage: obj.get_embedding() and returns: embedding