code: def test_sparse_inputs(self, act, kernel, do_stabilize):
      if do_stabilize and act != "relu":
          raise absltest.SkipTest(
              "Stabilization possible only in Relu."
          )
  
      key = random.PRNGKey(1)
  
      input_count = 4
      sparse_count = 2
      input_size = 3
      width = 1024
  
      # NOTE(schsam): It seems that convergence is slower when inputs are sparse.
      samples = N_SAMPLES
  
      if default_backend() == "gpu":
          tol = 5e-4
          samples = 100 * N_SAMPLES
      else:
          tol = {np.dtype(np.float32): 5e-2, np.dtype(np.float64): 5e-3}
  
      # a batch of dense inputs
      x_dense = random.normal(key, (input_count, input_size))
      x_sparse = x_dense.at[:sparse_count, :].set(0.0)
  
      activation = (
          stax.Relu(do_stabilize=do_stabilize)
          if act == "relu"
          else stax.Erf()
      )
  
      init_fn, apply_fn, kernel_fn = stax.serial(
          stax.Dense(width),
          activation,
          stax.Dense(1 if kernel == "ntk" else width),
      )
      exact = kernel_fn(x_sparse, None, kernel)
  
      mc = nt.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          random.split(key, 2)[0],
          samples,
          vmap_axes=0,
          device_count=-1,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )(x_sparse, None, kernel)
      mc = jnp.reshape(mc, exact.shape)
  
      assert not jnp.any(jnp.isnan(exact))
      self.assertAllClose(
          exact[sparse_count:, sparse_count:],
          mc[sparse_count:, sparse_count:],
          rtol=tol,
          atol=tol,
      )
  
imports: import functools
  import itertools
  import random
  from absl.testing import absltest
  from jax import config
  from jax import default_backend
  from jax import jit
  from jax import random
  from jax.example_libraries import stax
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from tests import test_utils
  import numpy
label_desc: Use image node_id 7 for calling the StaxTest obj's underlying member method code with example usage: obj.test_sparse_inputs(act, kernel, do_stabilize) without return types