class_docstr: An implementation of `"BANE" <https://shiruipan.github.io/publication/yang-binarized-2018/yang-binarized-2018.pdf>`_
  from the ICDM '18 paper "Binarized Attributed Network Embedding Class". The
  procedure first calculates the truncated SVD of an adjacency - feature matrix
  product. This matrix is further decomposed by a binary CCD based technique.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      svd_iterations (int): SVD iteration count. Default is 20.
      seed (int): Random seed. Default is 42.
      alpha (float): Kernel matrix inversion parameter. Default is 0.3.
      iterations (int): Matrix decomposition iterations. Default is 100.
      binarization_iterations (int): Binarization iterations. Default is 20.
      seed (int): Random seed value. Default is 42.
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docstr: Reducing the dimensionality with SVD in the 1st step.
docstr_tok: ["Reducing","the","dimensionality","with","SVD","in","the","1st","step","."]
code: def _fit_base_SVD_model(self):
      """
      Reducing the dimensionality with SVD in the 1st step.
      """
      self._P = self._P.dot(self._X)
      self.model = TruncatedSVD(
          n_components=self.dimensions,
          n_iter=self.svd_iterations,
          random_state=self.seed,
      )
  
      self.model.fit(self._P)
      self._P = self.model.fit_transform(self._P)
  
file_path: karateclub/karateclub/node_embedding/attributed/bane.py
imports: import numpy
  import networkx
  from typing import Union
  from numpy.linalg import inv
  from scipy.sparse import coo_matrix
  from sklearn.decomposition import TruncatedSVD
  from karateclub.estimator import Estimator
label_desc: Use image node_id 4 for calling the BANE obj's underlying member method code with example usage: obj._fit_base_SVD_model() without return types