class_docstr: Speeds up inference calls to MatrixFactorization models by using
  `NMSLib <https://github.com/nmslib/nmslib>`_ to create approximate nearest neighbours
  indices of the latent factors.
  
  Parameters
  ----------
  model : MatrixFactorizationBase
      A matrix factorization model to use for the factors
  method : str, optional
      The NMSLib method to use
  index_params: dict, optional
      Optional params to send to the createIndex call in NMSLib
  query_params: dict, optional
      Optional query time params for the NMSLib 'setQueryTimeParams' call
  approximate_similar_items : bool, optional
      whether or not to build an NMSLIB index for computing similar_items
  approximate_recommend : bool, optional
      whether or not to build an NMSLIB index for the recommend call
  
  Attributes
  ----------
  similar_items_index : nmslib.FloatIndex
      NMSLib index for looking up similar items in the cosine space formed by the latent
      item_factors
  
  recommend_index : nmslib.FloatIndex
      NMSLib index for looking up similar items in the inner product space formed by the latent
      item_factors
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code: def save(self, file):
      raise NotImplementedError(
          ".save isn't implemented for NMSLib yet"
      )
  
file_path: implicit/implicit/ann/nmslib.py
imports: import logging
  import nmslib
  import numpy
  from scipy.sparse import csr_matrix
  import implicit.gpu
  from implicit.recommender_base import RecommenderBase
  from implicit.utils import _batch_call, _filter_items_from_results, augment_inner_product_matrix
label_desc: Use image node_id 6 for calling the NMSLibModel obj's underlying member method code with example usage: obj.save(file) without return types