code: def testPredictOnCPU(self):
      x_train = random.normal(random.PRNGKey(1), (4, 4, 4, 2))
      x_test = random.normal(random.PRNGKey(1), (8, 4, 4, 2))
  
      y_train = random.uniform(random.PRNGKey(1), (4, 2))
  
      _, _, kernel_fn = stax.serial(
          stax.Conv(1, (3, 3)),
          stax.Relu(),
          stax.Flatten(),
          stax.Dense(1),
      )
  
      for store_on_device in [False, True]:
          for device_count in [0, 1]:
              for get in [
                  "ntk",
                  "nngp",
                  ("nngp", "ntk"),
                  ("ntk", "nngp"),
              ]:
                  for x in [None, "x_test"]:
                      with self.subTest(
                          store_on_device=store_on_device,
                          device_count=device_count,
                          get=get,
                          x=x,
                      ):
                          kernel_fn_batched = nt.batch(
                              kernel_fn,
                              2,
                              device_count,
                              store_on_device,
                          )
                          predictor = (
                              predict.gradient_descent_mse_ensemble(
                                  kernel_fn_batched, x_train, y_train
                              )
                          )
  
                          x = x if x is None else x_test
                          predict_none = predictor(
                              None, x, get, compute_cov=True
                          )
                          predict_inf = predictor(
                              jnp.inf, x, get, compute_cov=True
                          )
                          self.assertAllClose(predict_none, predict_inf)
  
                          if x is not None:
                              on_cpu = (
                                  not store_on_device
                                  or jax.default_backend() == "cpu"
                              )
  
                              def is_on_cpu(x):
                                  return jax.tree_util.tree_all(
                                      jax.tree_map(
                                          lambda x: "cpu"
                                          in str(
                                              x.addressable_shards[
                                                  0
                                              ].device
                                          ).lower(),
                                          x,
                                      )
                                  )
  
                              self.assertEqual(
                                  on_cpu, is_on_cpu(predict_inf)
                              )
                              self.assertEqual(
                                  on_cpu, is_on_cpu(predict_none)
                              )
  
imports: import math
  from absl.testing import absltest
  from jax import grad
  from jax import jit
  from jax import random
  from jax import vmap
  from jax import config
  from jax.example_libraries import optimizers
  from jax.flatten_util import ravel_pytree
  import jax.numpy
  import jax.tree_util
  import neural_tangents
  from neural_tangents import predict, stax
  from neural_tangents._src.predict import _is_on_cpu
  from tests import test_utils
label_desc: Use image node_id 17 for calling the PredictTest obj's underlying member method code with example usage: obj.testPredictOnCPU() and returns: jax