class_docstr: RLRMC algorithm implementation.
class_docstr_tok: ["RLRMC","algorithm","implementation","."]
code: def _init_train(self, entries_train_csr):
      logger.info("Hyper-parameters of the algorithm")
      logger.info(
          "Rank: %i, Regularization parameter: %e" % (self.rank, self.C)
      )
      # Initialization # starting point on the manifold
      if self.initialize_flag == "random":  # rndom
          W0 = None
      elif self.initialize_flag == "svd":  # svd
          U0, B0, V0 = svds(entries_train_csr, k=self.rank)
          W0 = [U0, V0.T, np.diag(B0)]
      else:  # default option when given incorrect option
          logger.warning(
              "Initialization flag not recognized. Setting it to random (default)."
          )
          W0 = None
      return W0
  
imports: import numpy
  import logging
  from pymanopt import Problem
  from recommenders.models.rlrmc.conjugate_gradient_ms import ConjugateGradientMS
  from pymanopt.solvers.linesearch import LineSearchBackTracking
  from pymanopt.manifolds import Stiefel, SymmetricPositiveDefinite, Product
  from math import sqrt
  from scipy.sparse import csr_matrix
  from scipy.sparse.linalg import svds
  from numba import njit, prange
label_desc: Use image node_id 2 for calling the RLRMCalgorithm obj's underlying member method code with example usage: obj._init_train(entries_train_csr) and returns: W0