code: def _always_ntk(cls, ker_fun):
      def always_ntk(x1, x2, get=("nngp", "ntk")):
          out = ker_fun(x1, x2, get=("nngp", "ntk"))
          if get == "nngp" or get == "ntk":
              return out.ntk
          else:
              return out._replace(nngp=out.ntk)
  
      return always_ntk
  
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 11 for calling the PredictTest obj's underlying member method code with example usage: obj._always_ntk(cls, ker_fun) and returns: always_ntk, out, out