code: def _theoretical_kernel(key, input_shape, network, out_logits):
      init_fn, f, kernel_fn = _build_network(
          input_shape, network, out_logits
      )
      _, params = init_fn(key, (-1,) + input_shape)
      return params, f, jit(kernel_fn, static_argnames="get")
  
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 3 for calling a global function with example usage: _theoretical_kernel(key, input_shape, network, out_logits) and returns: params,  f,  jit