class_docstr: An implementation of `"NMF-ADMM" <http://statweb.stanford.edu/~dlsun/papers/nmf_admm.pdf>`_
  from the ICASSP '14 paper "Alternating Direction Method of Multipliers for
  Non-Negative Matrix Factorization with the Beta-Divergence". The procedure
  learns an embedding of the normalized adjacency matrix with by using the alternating
  direction method of multipliers to solve a non negative matrix factorization problem.
  
  Args:
      dimensions (int): Number of individual embedding dimensions. Default is 32.
      iterations (int): Number of ADMM iterations. Default is 100.
      rho (float): ADMM Tuning parameter. Default is 1.0.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self,
      dimensions: int = 32,
      iterations: int = 100,
      rho: float = 1.0,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.iterations = iterations
      self.rho = rho
      self.seed = seed
  
imports: import numpy
  import scipy
  import networkx
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new NMFADMM object from inherited base classes: Estimator with example: obj = NMFADMM(dimensions, iterations, rho, seed)