class_docstr: An implementation of `"DeepWalk" <https://arxiv.org/abs/1403.6652>`_
  from the KDD '14 paper "DeepWalk: Online Learning of Social Representations".
  The procedure uses random walks to approximate the pointwise mutual information
  matrix obtained by pooling normalized adjacency matrix powers. This matrix
  is decomposed by an approximate factorization technique.
  
  Args:
      walk_number (int): Number of random walks. Default is 10.
      walk_length (int): Length of random walks. Default is 80.
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      window_size (int): Matrix power order. Default is 5.
      epochs (int): Number of epochs. Default is 1.
      use_hierarchical_softmax (bool): Whether to use hierarchical softmax or negative sampling to train the model. Default is True.
      number_of_negative_samples (int): Number of negative nodes to sample (usually between 5-20). If set to 0, no negative sampling is used. Default is 5.
      learning_rate (float): HogWild! learning rate. Default is 0.05.
      min_count (int): Minimal count of node occurrences. Default is 1.
      seed (int): Random seed value. Default is 42.
docstr: Getting the node embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of nodes.
code: def get_embedding(self) -> np.array:
      r"""Getting the node embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of nodes.
      """
      return np.array(self._embedding)
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/deepwalk.py
imports: import numpy
  import networkx
  from gensim.models.word2vec import Word2Vec
  from karateclub.utils.walker import RandomWalker
  from karateclub.estimator import Estimator
label_desc: Use image node_id 3 for calling the DeepWalk obj's underlying member method code with example usage: obj.get_embedding() and returns: np