code: def test_empirical_ntk_diagonal_outputs(
      self, same_inputs, device_count, trace_axes, diagonal_axes
  ):
      if any(t in diagonal_axes for t in trace_axes):
          raise absltest.SkipTest("Overlapping axes.")
  
      test_utils.stub_out_pmap(batching, 2)
      rng = random.PRNGKey(0)
  
      input_key1, input_key2, net_key = random.split(rng, 3)
  
      init_fn, apply_fn, _ = stax.serial(
          stax.Dense(5), stax.Relu(), stax.Dense(3)
      )
  
      test_x1 = random.normal(input_key1, (12, 4, 4))
      test_x2 = None
      if same_inputs:
          test_x2 = random.normal(input_key2, (9, 4, 4))
  
      kernel_fn = nt.empirical_ntk_fn(
          apply_fn,
          trace_axes=trace_axes,
          diagonal_axes=diagonal_axes,
          vmap_axes=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
  
      _, params = init_fn(net_key, test_x1.shape)
  
      true_kernel = kernel_fn(test_x1, test_x2, params)
      batched_fn = batching.batch(
          kernel_fn, device_count=device_count, batch_size=3
      )
      batch_kernel = batched_fn(test_x1, test_x2, params)
      self.assertAllClose(true_kernel, batch_kernel)
  
imports: from functools import partial
  from absl.testing import absltest
  from jax import jit
  from jax import config
  from jax import random
  import jax.numpy
  from jax.tree_util import tree_map
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src import batching
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from tests import test_utils
label_desc: Use image node_id 13 for calling the BatchTest obj's underlying member method code with example usage: obj.test_empirical_ntk_diagonal_outputs(same_inputs, device_count, trace_axes, diagonal_axes) without return types