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 recommend(
      self,
      userid,
      user_items,
      N=10,
      filter_already_liked_items=True,
      filter_items=None,
      recalculate_user=False,
      items=None,
  ):
      if filter_already_liked_items or recalculate_user:
          if not isinstance(user_items, csr_matrix):
              raise ValueError(
                  "user_items needs to be a CSR sparse matrix"
              )
          user_count = 1 if np.isscalar(userid) else len(userid)
          if user_items.shape[0] != user_count:
              raise ValueError(
                  "user_items must contain 1 row for every user in userids"
              )
  
      if recalculate_user:
          user_factors = self.recalculate_user(userid, user_items)
      else:
          user_factors = self.user_factors[userid]
  
      item_factors = self.item_factors
      if items is not None:
          N = min(N, len(items))
          if filter_items:
              raise ValueError(
                  "Can't set both items and filter_items in recommend call"
              )
  
          items = np.array(items)
          items.sort()
          item_factors = item_factors[items]
  
          # check selected items are in the model
          if (
              items.max() >= self.item_factors.shape[0]
              or items.min() < 0
          ):
              raise IndexError("Some itemids are not in the model")
  
      if filter_items is not None:
          filter_items = implicit.gpu.IntVector(
              np.array(filter_items, dtype="int32")
          )
  
      query_filter = None
      if filter_already_liked_items:
          query_filter = user_items
  
          # if we've been given a list of explicit itemids to rank, we need to filter down
          if items is not None:
              query_filter = _filter_items_from_sparse_matrix(
                  items, query_filter
              )
  
          if query_filter.nnz:
              query_filter = implicit.gpu.COOMatrix(
                  query_filter.tocoo()
              )
          else:
              query_filter = None
  
      # calculate the top N items, removing the users own liked items from the results
      ids, scores = self.knn.topk(
          item_factors,
          user_factors,
          N,
          query_filter=query_filter,
          item_filter=filter_items,
      )
  
      if np.isscalar(userid):
          ids, scores = ids[0], scores[0]
  
      if items is not None:
          ids = items[ids]
  
      return ids, scores
  
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 2 for calling the MatrixFactorizationBase obj's underlying member method code with example usage: obj.recommend(userid, user_items, N, filter_already_liked_items, filter_items, recalculate_user, items) and returns: ids,  scores