class_docstr: An implementation of `"GraRep" <https://dl.acm.org/citation.cfm?id=2806512>`_
  from the CIKM '15 paper "GraRep: Learning Graph Representations with Global
  Structural Information". The procedure uses sparse truncated SVD to learn
  embeddings for the powers of the PMI matrix computed from powers of the
  normalized adjacency matrix.
  
  Args:
      dimensions (int): Number of individual embedding dimensions. Default is 32.
      iteration (int): Number of SVD iterations. Default is 10.
      order (int): Number of PMI matrix powers. Default is 5.
      seed (int): SVD random seed. Default is 42.
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docstr: Getting the node embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of nodes.
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code: def get_embedding(self) -> np.array:
      r"""Getting the node embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of nodes.
      """
      embedding = np.concatenate(self._embeddings, axis=1)
      return embedding
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/grarep.py
imports: import math
  import numpy
  import networkx
  from scipy import sparse
  from sklearn.decomposition import TruncatedSVD
  from karateclub.estimator import Estimator
label_desc: Use image node_id 7 for calling the GraRep obj's underlying member method code with example usage: obj.get_embedding() and returns: embedding