class_docstr: Simple implementation of "Exact Matrix Completion via Convex Optimization"
  by Emmanuel Candes and Benjamin Recht using cvxpy.
docstr: Parameters
  ----------
  m, n : int
      Dimensions that of solution matrix
  Returns the objective function and a variable representing the
  solution to the convex optimization problem.
code: def _create_objective(self, m, n):
      """
      Parameters
      ----------
      m, n : int
          Dimensions that of solution matrix
      Returns the objective function and a variable representing the
      solution to the convex optimization problem.
      """
      # S is the completed matrix
      shape = (m, n)
      S = cvxpy.Variable(shape, name="S")
      norm = cvxpy.norm(S, "nuc")
      objective = cvxpy.Minimize(norm)
      return S, objective
  
file_path: fancyimpute/fancyimpute/nuclear_norm_minimization.py
imports: import cvxpy
  from .solver import Solver
  from sklearn.utils import check_array
label_desc: Use image node_id 3 for calling the NuclearNormMinimization obj's underlying member method code with example usage: obj._create_objective(m, n) and returns: S,  objective