code: def AlternatingLeastSquares(
      factors=100,
      regularization=0.01,
      alpha=1.0,
      dtype=np.float32,
      use_native=True,
      use_cg=True,
      use_gpu=implicit.gpu.HAS_CUDA,
      iterations=15,
      calculate_training_loss=False,
      num_threads=0,
      random_state=None,
  ):
      """Alternating Least Squares
  
      A Recommendation Model based off the algorithms described in the paper 'Collaborative
      Filtering for Implicit Feedback Datasets' with performance optimizations described in
      'Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative
      Filtering.'
  
      This factory function switches between the cpu and gpu implementations found in
      implicit.cpu.als.AlternatingLeastSquares and implicit.gpu.als.AlternatingLeastSquares
      depending on the use_gpu flag.
  
      Parameters
      ----------
      factors : int, optional
          The number of latent factors to compute
      regularization : float, optional
          The regularization factor to use
      alpha : float, optional
          The weight to give to positive examples.
      dtype : data-type, optional
          Specifies whether to generate 64 bit or 32 bit or 16 bit floating point factors
      use_native : bool, optional
          Use native extensions to speed up model fitting
      use_cg : bool, optional
          Use a faster Conjugate Gradient solver to calculate factors
      use_gpu : bool, optional
          Fit on the GPU if available, default is to run on GPU only if available
      iterations : int, optional
          The number of ALS iterations to use when fitting data
      calculate_training_loss : bool, optional
          Whether to log out the training loss at each iteration
      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, np.random.RandomState or None, optional
          The random state for seeding the initial item and user factors.
          Default is None.
      """
      if use_gpu:
          return implicit.gpu.als.AlternatingLeastSquares(
              factors,
              regularization,
              alpha,
              dtype=dtype,
              iterations=iterations,
              calculate_training_loss=calculate_training_loss,
              random_state=random_state,
          )
      return implicit.cpu.als.AlternatingLeastSquares(
          factors,
          regularization,
          alpha,
          dtype,
          use_native,
          use_cg,
          iterations,
          calculate_training_loss,
          num_threads,
          random_state,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: AlternatingLeastSquares(factors, regularization, alpha, dtype, use_native, use_cg, use_gpu, iterations, calculate_training_loss, num_threads, random_state) and returns: implicit, implicit