code: def testSparseConvolve(
      self,
      lhs_shape=(10,),
      rhs_shape=(5,),
      dtype="float32",
      mode="full",
  ):
      # Note: more comprehensive tests in sparse_test.py:test_bcoo_conv_general_dilated
      dense_fun = partial(jnp.convolve, mode=mode)
      sparse_fun = self.sparsify(dense_fun)
  
      sprng = sptu.rand_bcoo(self.rng())
      rng = jtu.rand_default(self.rng())
  
      lhs = sprng(lhs_shape, dtype)
      rhs = rng(rhs_shape, dtype)
  
      expected = dense_fun(lhs.todense(), rhs)
      actual = sparse_fun(lhs, rhs).todense()
  
      tol = {
          np.float32: 1e-5,
          np.complex64: 1e-5,
          np.float64: 1e-14,
          np.complex128: 1e-14,
      }
      self.assertAllClose(expected, actual, atol=tol, rtol=tol)
  
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 21 for calling the SparsifyTest obj's underlying member method code with example usage: obj.testSparseConvolve(lhs_shape, rhs_shape, dtype, mode) without return types