code: def testSparseWhileLoopDuplicateIndices(self):
      def cond_fun(params):
          i, A, B = params
          return i < 5
  
      def body_fun(params):
          i, A, B = params
          # TODO(jakevdp): track shared indices through while loop & use this
          #   version of the test, which requires shared indices in order for
          #   the nse of the result to remain the same.
          # return i + 1, A, A + B
  
          # This version is fine without shared indices, and tests that we're
          # flattening non-shared indices consistently.
          return i + 1, B, A
  
      def f(A):
          return lax.while_loop(cond_fun, body_fun, (0, A, A))
  
      A = jnp.arange(4).reshape((2, 2))
      out_dense = f(A)
  
      Asp = BCOO.fromdense(A)
      out_sparse = self.sparsify(f)(Asp)
  
      self.assertEqual(len(out_dense), 3)
      self.assertEqual(len(out_sparse), 3)
      self.assertArraysEqual(out_dense[0], out_dense[0])
      self.assertArraysEqual(out_dense[1], out_sparse[1].todense())
      self.assertArraysEqual(out_dense[2], out_sparse[2].todense())
  
imports: from functools import partial
  import math
  import operator
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import jax
  from jax import config, jit, lax
  import jax.numpy
  import jax._src.test_util
  from jax.experimental.sparse import BCOO, BCSR, sparsify, todense, SparseTracer
  from jax.experimental.sparse.transform import arrays_to_spvalues, spvalues_to_arrays, sparsify_raw, SparsifyValue, SparsifyEnv
  from jax.experimental.sparse.util import CuSparseEfficiencyWarning
  from jax.experimental.sparse import test_util
label_desc: Use image node_id 24 for calling the SparsifyTest obj's underlying member method code with example usage: obj.testSparseWhileLoopDuplicateIndices() and returns: unknown, unknown,  B,  A, lax