class_docstr: LightGCN
  
  Parameters
  ----------
  name: string, default: 'LightGCN'
      The name of the recommender model.
  
  emb_size: int, default: 64
      Size of the node embeddings.
  
  num_epochs: int, default: 1000
      Maximum number of iterations or the number of epochs.
  
  learning_rate: float, default: 0.001
      The learning rate that determines the step size at each iteration
  
  batch_size: int, default: 1024
      Mini-batch size used for train set
  
  num_layers: int, default: 3
      Number of LightGCN Layers
  
  early_stopping: {min_delta: float, patience: int}, optional, default: None
      If `None`, no early stopping. Meaning of the arguments:
  
      - `min_delta`:  the minimum increase in monitored value on validation
                      set to be considered as improvement,
                      i.e. an increment of less than min_delta will count as
                      no improvement.
  
      - `patience`:   number of epochs with no improvement after which
                      training should be stopped.
  
  lambda_reg: float, default: 1e-4
      Weight decay for the L2 normalization
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model
      is already pre-trained.
  
  verbose: boolean, optional, default: False
      When True, some running logs are displayed.
  
  seed: int, optional, default: 2020
      Random seed for parameters initialization.
  
  References
  ----------
  *   He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020).
      LightGCN: Simplifying and Powering Graph Convolution Network for
      Recommendation.
docstr: Getting a matrix of user vectors serving as query for ANN search.
  
  Returns
  -------
  out: numpy.array
      Matrix of user vectors for all users available in the model.
docstr_tok: ["Getting","a","matrix","of","user","vectors","serving","as","query","for","ANN","search",".","Returns","--","--","--","-","out",":","numpy.array","Matrix","of","user","vectors","for","all","users","available","in","the","model","."]
code: def get_user_vectors(self):
      """Getting a matrix of user vectors serving as query for ANN search.
  
      Returns
      -------
      out: numpy.array
          Matrix of user vectors for all users available in the model.
      """
      return self.U
  
file_path: cornac/cornac/models/lightgcn/recom_lightgcn.py
imports: from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
  from tqdm.auto import tqdm, trange
label_desc: Use image node_id 6 for calling the LightGCN obj's underlying member method code with example usage: obj.get_user_vectors() and returns: self