class_docstr: Speeds up inference calls to MatrixFactorization models by using
  `Faiss <https://github.com/facebookresearch/faiss>`_ to create approximate nearest neighbours
  indices of the latent factors.
  
  Parameters
  ----------
  model : MatrixFactorizationBase
      A matrix factorization model to use for the factors
  nlist : int, optional
      The number of cells to use when building the Faiss index.
  nprobe : int, optional
      The number of cells to visit to perform a search.
  use_gpu : bool, optional
      Whether or not to enable run Faiss on the GPU. Requires faiss to have been
      built with GPU support.
  approximate_similar_items : bool, optional
      whether or not to build an Faiss index for computing similar_items
  approximate_recommend : bool, optional
      whether or not to build an Faiss index for the recommend call
  
  Attributes
  ----------
  similar_items_index : faiss.IndexIVFFlat
      Faiss index for looking up similar items in the cosine space formed by the latent
      item_factors
  
  recommend_index : faiss.IndexIVFFlat
      Faiss index for looking up similar items in the inner product space formed by the latent
      item_factors
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code: def load(cls, file):
      raise NotImplementedError(".load isn't implemented for Faiss yet")
  
file_path: implicit/implicit/ann/faiss.py
imports: import logging
  import warnings
  import faiss
  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
label_desc: Use image node_id 7 for calling the FaissModel obj's underlying member method code with example usage: obj.load(cls, file) without return types