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 __setstate__(self, state):
      self.__dict__.update(state)
      if self.item_factors is not None:
          self.item_factors = implicit.gpu.Matrix(self.item_factors)
      if self.user_factors is not None:
          self.user_factors = implicit.gpu.Matrix(self.user_factors)
  
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 13 for calling the MatrixFactorizationBase obj's underlying member method code with example usage: obj.__setstate__(state) without return types