class_docstr: An implementation of `"TENE" <https://ieeexplore.ieee.org/document/8545577>`_
  from the ICPR '18 paper "Enhanced Network Embedding with Text Information". The
  procedure jointly factorizes the adjacency and node feature matrices using alternating
  least squares.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      lower_control (float): Embedding score minimal value. Default is 10**-15.
      alpha (float): Adjacency matrix regularization coefficient. Default is 0.1.
      beta (float): Feature matrix regularization coefficient. Default is 0.1.
      iterations (int): ALS iterations. Default is 200.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self,
      dimensions=32,
      lower_control=10**-15,
      alpha=0.1,
      beta=0.1,
      iterations=200,
      seed=42,
  ):
      self.dimensions = dimensions
      self.lower_control = lower_control
      self.alpha = alpha
      self.beta = beta
      self.iterations = iterations
      self.seed = seed
  
file_path: karateclub/karateclub/node_embedding/attributed/tene.py
imports: import numpy
  import networkx
  from typing import Union
  from scipy import sparse
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new TENE object from inherited base classes: Estimator with example: obj = TENE(dimensions, lower_control, alpha, beta, iterations, seed)