code: def test_rank1_symmetric_convex_solver():
      XYXY_rank1, XYXY_missing_rank1 = create_rank1_data(symmetric=True)
      solver = NuclearNormMinimization(require_symmetric_solution=True)
      completed = solver.fit_transform(XYXY_missing_rank1)
      assert (
          abs(completed[1, 2] - XYXY_rank1[1, 2]) < 0.01
      ), "Expected %0.4f but got %0.4f" % (
          XYXY_rank1[1, 2],
          completed[1, 2],
      )
  
imports: from fancyimpute import NuclearNormMinimization
  import numpy
  from low_rank_data import XY, XY_incomplete, missing_mask
  from common import reconstruction_error
label_desc: Use image node_id 3 for calling a global function with example usage: test_rank1_symmetric_convex_solver() without return types