code: def LogisticMatrixFactorization(
      factors=30,
      learning_rate=1.00,
      regularization=0.6,
      dtype=np.float32,
      iterations=30,
      neg_prop=30,
      use_gpu=False,
      num_threads=0,
      random_state=None,
  ):
      """Logistic Matrix Factorization
  
      A collaborative filtering recommender model that learns probabilistic distribution
      whether user like it or not. Algorithm of the model is described in
      `Logistic Matrix Factorization for Implicit Feedback Data
      <https://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf>`
  
      Parameters
      ----------
      factors : int, optional
          The number of latent factors to compute
      learning_rate : float, optional
          The learning rate to apply for updates during training
      regularization : float, optional
          The regularization factor to use
      dtype : data-type, optional
          Specifies whether to generate 64 bit or 32 bit floating point factors
      iterations : int, optional
          The number of training epochs to use when fitting the data
      neg_prop : int, optional
          The proportion of negative samples. i.e.) "neg_prop = 30" means if user have seen 5 items,
          then 5 * 30 = 150 negative samples are used for training.
      use_gpu : bool, optional
          Fit on the GPU if available
      num_threads : int, optional
          The number of threads to use for fitting the model. This only
          applies for the native extensions. Specifying 0 means to default
          to the number of cores on the machine.
      random_state : int, RandomState or None, optional
          The random state for seeding the initial item and user factors.
          Default is None.
  
      Attributes
      ----------
      item_factors : ndarray
          Array of latent factors for each item in the training set
      user_factors : ndarray
          Array of latent factors for each user in the training set
      """
      if use_gpu:
          raise NotImplementedError
      return implicit.cpu.lmf.LogisticMatrixFactorization(
          factors,
          learning_rate,
          regularization,
          dtype=dtype,
          iterations=iterations,
          neg_prop=neg_prop,
          num_threads=num_threads,
          random_state=random_state,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: LogisticMatrixFactorization(factors, learning_rate, regularization, dtype, iterations, neg_prop, use_gpu, num_threads, random_state) and returns: implicit