class_docstr: Base class for a recommender model supporting Approximate Nearest Neighbor (ANN) search.
  
  Parameters
  ----------------
  model: object: :obj:`cornac.models.Recommender`, required
      Trained recommender model which to get user/item vectors from.
  
  name: str, required
      Name of the recommender model.
  
  verbose: boolean, optional, default: False
      When True, running logs are displayed.
class_docstr_tok: ["Base","class","for","a","recommender","model","supporting","Approximate","Nearest","Neighbor","(","ANN",")","search",".","Parameters","--","--","--","--","--","--","--","--","model",":","object",":",":","obj",":","`","cornac.models.Recommender","`",",","required","Trained","recommender","model","which","to","get","user\/item","vectors","from",".","name",":","str",",","required","Name","of","the","recommender","model",".","verbose",":","boolean",",","optional",",","default",":","False","When","True",",","running","logs","are","displayed","."]
docstr: Implementing ANN search for a given query.
  
  Returns
  -------
  :raise NotImplementedError
docstr_tok: ["Implementing","ANN","search","for","a","given","query",".","Returns","--","--","--","-",":","raise","NotImplementedError"]
code: def knn_query(self, query, k):
      """Implementing ANN search for a given query.
  
      Returns
      -------
      :raise NotImplementedError
      """
      raise NotImplementedError()
  
file_path: cornac/cornac/models/ann/recom_ann_base.py
imports: import copy
  import warnings
  import numpy
  from ..recommender import Recommender
  from ..recommender import is_ann_supported
  from ..recommender import MEASURE_DOT, MEASURE_COSINE
label_desc: Use image node_id 4 for calling the BaseANN obj's underlying member method code with example usage: obj.knn_query(query, k) without return types