class_docstr: An implementation of `"First-order LINE" <https://arxiv.org/abs/1503.03578>`_
  from the paper "LINE: Large-scale Information Network Embedding".
  
  Args:
      dimensions (int): Dimensionality of embedding. Default is 128.
      epochs (int): Number of epochs. Default is 100.
      mini_batch_size (int): Number of samples in each mini-batch. Default is 128.
      learning_rate (float): learning rate. Default is 0.05.
      learning_rate_decay (float): learning rate decay per epoch. Default is 0.999.
      verbose (bool): whether to show a loading bar using TQDM while queering.
      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 self.embedding
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/first_order_line.py
imports: import numpy
  import networkx
  from karateclub.estimator import Estimator
  from tqdm.auto import trange
label_desc: Use image node_id 4 for calling the FirstOrderLINE obj's underlying member method code with example usage: obj.get_embedding() and returns: self