class_docstr: Base class for MF models running on the GPU.
  
  This adds support for inference to run on the GPU as well as training.
  
  Attributes
  ----------
  item_factors : implicit.gpu.Matrix
      Array of latent factors for each item in the training set
  user_factors : implicit.gpu.Matrix
      Array of latent factors for each user in the training set
code: def __init__(self):
      self.item_factors = None
      self.user_factors = None
      self._item_norms = None
      self._user_norms = None
      self._user_norms_host = None
      self._item_norms_host = None
      self._knn = None
  
imports: import time
  import numpy
  from scipy.sparse import csr_matrix
  import implicit.gpu
  from ..cpu.matrix_factorization_base import _filter_items_from_sparse_matrix
  from ..recommender_base import RecommenderBase
label_desc: Use image node_id 1 to create a new MatrixFactorizationBase object from inherited base classes: RecommenderBase with example: obj = MatrixFactorizationBase()