class_docstr: RLRMC algorithm implementation.
class_docstr_tok: ["RLRMC","algorithm","implementation","."]
docstr: Main fit and evalute method for RLRMC. In addition to fitting the model, it also computes the per
  iteration statistics in train (and validation) datasets.
  
  Args:
      RLRMCdata (RLRMCdataset): the RLRMCdataset object.
      verbosity (int): verbosity of Pymanopt. Possible values are 0 (least verbose), 1, or 2 (most verbose).
docstr_tok: ["Main","fit","and","evalute","method","for","RLRMC",".","In","addition","to","fitting","the","model",",","it","also","computes","the","per","iteration","statistics","in","train","(","and","validation",")","datasets",".","Args",":","RLRMCdata","(","RLRMCdataset",")",":","the","RLRMCdataset","object",".","verbosity","(","int",")",":","verbosity","of","Pymanopt",".","Possible","values","are","0","(","least","verbose",")",",","1",",","or","2","(","most","verbose",")","."]
code: def fit_and_evaluate(self, RLRMCdata, verbosity=0):
      """Main fit and evalute method for RLRMC. In addition to fitting the model, it also computes the per
      iteration statistics in train (and validation) datasets.
  
      Args:
          RLRMCdata (RLRMCdataset): the RLRMCdataset object.
          verbosity (int): verbosity of Pymanopt. Possible values are 0 (least verbose), 1, or 2 (most verbose).
      """
      # it calls fit method with appropriate arguments
      self.fit(RLRMCdata, verbosity, True)
  
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 3 for calling the RLRMCalgorithm obj's underlying member method code with example usage: obj.fit_and_evaluate(RLRMCdata, verbosity) without return types