code: def test_is_on_cpu(self):
      dtypes = [jnp.float16, jnp.float32]
      float64 = jax.dtypes.canonicalize_dtype(jnp.float64)
      if float64 != jnp.float32:
          dtypes += [float64]
  
      for dtype in dtypes:
          with self.subTest(dtype=dtype):
  
              def x():
                  return random.normal(random.PRNGKey(1), (2, 3), dtype)
  
              def x_cpu():
                  return jax.device_get(
                      random.normal(random.PRNGKey(1), (2, 3), dtype)
                  )
  
              x_jit = jit(x)
              # x_cpu_jit = jit(x_cpu)
              x_cpu_jit_cpu = jit(x_cpu, backend="cpu")
  
              self.assertTrue(_is_on_cpu(x_cpu()))
              # TODO(mattjj): re-enable this when device_put under jit works
              # self.assertTrue(predict._is_on_cpu(x_cpu_jit()))
              self.assertTrue(_is_on_cpu(x_cpu_jit_cpu()))
  
              if jax.default_backend() == "cpu":
                  self.assertTrue(_is_on_cpu(x()))
                  self.assertTrue(_is_on_cpu(x_jit()))
              else:
                  self.assertFalse(_is_on_cpu(x()))
                  self.assertFalse(_is_on_cpu(x_jit()))
  
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 1 for calling the IsOnCpuTest obj's underlying member method code with example usage: obj.test_is_on_cpu() and returns: random, jax