class_docstr: An implementation of `"DANMF" <https://smartyfh.com/Documents/18DANMF.pdf>`_
  from the CIKM '18 paper "Deep Autoencoder-like Nonnegative Matrix Factorization for
  Community Detection". The procedure uses telescopic non-negative matrix factorization
  in order to learn a cluster membership distribution over nodes. The method can be
  used in an overlapping and non-overlapping way.
  
  Args:
      layers (list): Autoencoder layer sizes in a list of integers. Default [32, 8].
      pre_iterations (int): Number of pre-training epochs. Default 100.
      iterations (int): Number of training epochs. Default 100.
      seed (int): Random seed for weight initializations. Default 42.
      lamb (float): Regularization parameter. Default 0.01.
      seed (int): Random seed value. Default is 42.
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docstr: Getting the bottleneck layer embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The bottleneck layer embedding of nodes.
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code: def get_embedding(self) -> np.array:
      r"""Getting the bottleneck layer embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The bottleneck layer embedding of nodes.
      """
      embedding = [self._P, self._V_s[-1].T]
      embedding = np.concatenate(embedding, axis=1)
      return embedding
  
imports: import numpy
  import networkx
  from typing import List, Dict
  from sklearn.decomposition import NMF
  from karateclub.estimator import Estimator
label_desc: Use image node_id 12 for calling the DANMF obj's underlying member method code with example usage: obj.get_embedding() and returns: embedding