code: def testSparseMul(
      self, shape, dtype, n_batch, n_dense, unique_indices
  ):
      rng_sparse = rand_sparse(
          self.rng(), rand_method=jtu.rand_some_zero
      )
      x = BCOO.fromdense(
          rng_sparse(shape, dtype), n_batch=n_batch, n_dense=n_dense
      )
  
      # Scalar multiplication
      scalar = 2
      y = self.sparsify(operator.mul)(x, scalar)
      self.assertArraysEqual(x.todense() * scalar, y.todense())
  
      # Shared indices – requires lower level call
      spenv = SparsifyEnv([x.indices, x.data, y.data])
      spvalues = [
          spenv.sparse(
              x.shape,
              data_ref=1,
              indices_ref=0,
              unique_indices=unique_indices,
          ),
          spenv.sparse(
              y.shape,
              data_ref=2,
              indices_ref=0,
              unique_indices=unique_indices,
          ),
      ]
  
      result = sparsify_raw(operator.mul)(spenv, *spvalues)
      args_out, _ = result
      (out,) = spvalues_to_arrays(spenv, args_out)
  
      self.assertAllClose(out.todense(), x.todense() * y.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 12 for calling the SparsifyTest obj's underlying member method code with example usage: obj.testSparseMul(shape, dtype, n_batch, n_dense, unique_indices) without return types