code: def test_jit_or_pmap_broadcast(self):
      def kernel_fn(
          x1, x2, do_flip, keys, do_square, params, _unused=None, p=0.65
      ):
          res = jnp.abs(jnp.matmul(x1, x2))
          if do_square:
              res *= res
          if do_flip:
              res = -res
  
          res *= random.uniform(keys) * p
          return [res, params]
  
      params = (
          jnp.array([1.0, 0.3]),
          (jnp.array([1.2]), jnp.array([0.5])),
      )
      x2 = jnp.arange(0, 10).reshape((10,))
      keys = random.PRNGKey(1)
  
      kernel_fn_pmapped = batching._jit_or_pmap_broadcast(
          kernel_fn, device_count=0
      )
      x1 = jnp.arange(0, 10).reshape((1, 10))
      for do_flip in [True, False]:
          for do_square in [True, False]:
              with self.subTest(
                  do_flip=do_flip, do_square=do_square, device_count=0
              ):
                  res_1 = kernel_fn(
                      x1,
                      x2,
                      do_flip,
                      keys,
                      do_square,
                      params,
                      _unused=True,
                      p=0.65,
                  )
                  res_2 = kernel_fn_pmapped(
                      x1,
                      x2,
                      do_flip,
                      keys,
                      do_square,
                      params,
                      _unused=True,
                  )
                  self.assertAllClose(res_1, res_2)
  
      test_utils.stub_out_pmap(batching, 1)
      x1 = jnp.arange(0, 10).reshape((1, 10))
      kernel_fn_pmapped = batching._jit_or_pmap_broadcast(
          kernel_fn, device_count=1
      )
      for do_flip in [True, False]:
          for do_square in [True, False]:
              with self.subTest(
                  do_flip=do_flip, do_square=do_square, device_count=1
              ):
                  res_1 = kernel_fn(
                      x1,
                      x2,
                      do_flip,
                      keys,
                      do_square,
                      params,
                      _unused=False,
                      p=0.65,
                  )
                  res_2 = kernel_fn_pmapped(
                      x1,
                      x2,
                      do_flip,
                      keys,
                      do_square,
                      params,
                      _unused=None,
                  )
                  self.assertAllClose(res_1[0], res_2[0])
                  self.assertAllClose(
                      tree_map(
                          partial(jnp.expand_dims, axis=0), res_1[1]
                      ),
                      res_2[1],
                  )
  
      kernel_fn_pmapped = batching._jit_or_pmap_broadcast(
          kernel_fn, device_count=2
      )
      x1 = jnp.arange(0, 20).reshape((2, 10))
      test_utils.stub_out_pmap(batching, 2)
  
      def broadcast(arg):
          return jnp.broadcast_to(arg, (2,) + arg.shape)
  
      for do_flip in [True, False]:
          for do_square in [True, False]:
              with self.subTest(
                  do_flip=do_flip, do_square=do_square, device_count=2
              ):
                  res_1 = kernel_fn(
                      x1, x2, do_flip, keys, do_square, params, p=0.2
                  )
                  res_2 = kernel_fn_pmapped(
                      x1,
                      x2,
                      do_flip,
                      keys,
                      do_square,
                      params,
                      _unused=None,
                      p=0.2,
                  )
                  self.assertAllClose(res_1[0][0], res_2[0][0])
                  self.assertAllClose(res_1[0][1], res_2[0][1])
                  self.assertAllClose(
                      tree_map(broadcast, res_1[1]), res_2[1]
                  )
  
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 10 for calling the BatchTest obj's underlying member method code with example usage: obj.test_jit_or_pmap_broadcast() and returns: list, jnp