class_docstr: An implementation of `"TADW" <https://www.ijcai.org/Proceedings/15/Papers/299.pdf>`_
  from the IJCAI '15 paper "Network Representation Learning with Rich Text Information". The
  procedure uses the node attribute matrix with a factorization matrix to reproduce a power
  of the adjacency matrix to create representations.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      reduction_dimensions (int): SVD reduction dimensions. Default is 64.
      svd_iterations (int): SVD iteration count. Default is 20.
      seed (int): Random seed. Default is 42.
      alpha (float): Learning rate. Default is 0.01.
      iterations (int): Matrix decomposition iterations. Default is 10.
      lambd (float): Regularization coefficient. Default is 10.0.
docstr: Initialization of weights and loss container.
docstr_tok: ["Initialization","of","weights","and","loss","container","."]
code: def _init_weights(self):
      """
      Initialization of weights and loss container.
      """
      self._W = np.random.uniform(
          0, 1, (self.dimensions, self._A.shape[0])
      )
      self._H = np.random.uniform(
          0, 1, (self.dimensions, self._T.shape[0])
      )
  
file_path: karateclub/karateclub/node_embedding/attributed/tadw.py
imports: import numpy
  import networkx
  from typing import Union
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
  from sklearn.decomposition import TruncatedSVD
label_desc: Use image node_id 3 for calling the TADW obj's underlying member method code with example usage: obj._init_weights() without return types