class_docstr: Embarrassingly Shallow Autoencoders for Sparse Data.
  
  Parameters
  ----------
  name: string, optional, default: 'EASEᴿ'
      The name of the recommender model.
  
  lamb: float, optional, default: 500
      L2-norm regularization-parameter λ ∈ R+.
  
  posB: boolean, optional, default: False
      Remove Negative Weights
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model is already         trained. 
      
  verbose: boolean, optional, default: False
      When True, some running logs are displayed.
  
  seed: int, optional, default: None
      Random seed for parameters initialization.
  
  References
  ----------
  * Steck, H. (2019, May). "Embarrassingly shallow autoencoders for sparse data."     In The World Wide Web Conference (pp. 3251-3257).
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docstr: Getting a matrix of item vectors used for building the index for ANN search.
  
  Returns
  -------
  out: numpy.array
      Matrix of item vectors for all items available in the model.
docstr_tok: ["Getting","a","matrix","of","item","vectors","used","for","building","the","index","for","ANN","search",".","Returns","--","--","--","-","out",":","numpy.array","Matrix","of","item","vectors","for","all","items","available","in","the","model","."]
code: def get_item_vectors(self):
      """Getting a matrix of item vectors used for building the index for ANN search.
  
      Returns
      -------
      out: numpy.array
          Matrix of item vectors for all items available in the model.
      """
      return self.B
  
file_path: cornac/cornac/models/ease/recom_ease.py
imports: import numpy
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
label_desc: Use image node_id 6 for calling the EASE obj's underlying member method code with example usage: obj.get_item_vectors() and returns: self