code: def testNTK_NTKNNGPAgreement(
      self,
      train_size,
      test_size,
      input_shape,
      network,
      out_logits,
  ):
      train_shape = (train_size, *input_shape)
      test_shape = (test_size, *input_shape)
      _, x_test, x_train, y_train = self._get_inputs(
          out_logits, test_shape, train_shape
      )
      _, _, ker_fun = _build_network(
          train_shape[1:], network, out_logits
      )
  
      reg = 1e-7
      predictor = predict.gradient_descent_mse_ensemble(
          ker_fun, x_train, y_train, diag_reg=reg
      )
  
      ts = jnp.logspace(-2, 8, 10).reshape((5, 2))
  
      for t in (None, "ts"):
          for x in (None, "x_test"):
              with self.subTest(t=t, x=x):
                  x = x if x is None else x_test
                  t = t if t is None else ts
  
                  ntk = predictor(t=t, get="ntk", x_test=x)
  
                  # Test time broadcasting
                  if t is not None:
                      ntk_ind = jnp.array(
                          [
                              predictor(t=t, get="ntk", x_test=x)
                              for t in t.ravel()
                          ]
                      ).reshape(t.shape + ntk.shape[2:])
                      self.assertAllClose(ntk_ind, ntk)
  
                  always_ntk = self._always_ntk(ker_fun)
                  predictor_ntk = predict.gradient_descent_mse_ensemble(
                      always_ntk, x_train, y_train, diag_reg=reg
                  )
  
                  ntk_nngp = predictor_ntk(t=t, get="nngp", x_test=x)
  
                  # Test if you use nngp equations with ntk, you get the same mean
                  self.assertAllClose(ntk, ntk_nngp)
  
                  # Next test that if you go through the NTK code path, but with only
                  # the NNGP kernel, we recreate the NNGP dynamics.
                  # Create a hacked kernel function that always returns the nngp kernel
                  def always_nngp(x1, x2, get=("nngp", "ntk")):
                      out = ker_fun(x1, x2, get=("nngp", "ntk"))
                      if get == "nngp" or get == "ntk":
                          return out.nngp
                      else:
                          return out._replace(ntk=out.nngp)
  
                  predictor_nngp = (
                      predict.gradient_descent_mse_ensemble(
                          always_nngp, x_train, y_train, diag_reg=reg
                      )
                  )
  
                  nngp_cov = predictor(
                      t=t, get="nngp", x_test=x, compute_cov=True
                  ).covariance
  
                  # test time broadcasting for covariance
                  nngp_ntk_cov = predictor_nngp(
                      t=t, get="ntk", x_test=x, compute_cov=True
                  ).covariance
                  if t is not None:
                      nngp_ntk_cov_ind = jnp.array(
                          [
                              predictor_nngp(
                                  t=t,
                                  get="ntk",
                                  x_test=x,
                                  compute_cov=True,
                              ).covariance
                              for t in t.ravel()
                          ]
                      ).reshape(t.shape + nngp_cov.shape[2:])
                      self.assertAllClose(
                          nngp_ntk_cov_ind, nngp_ntk_cov
                      )
  
                  # Test if you use ntk equations with nngp, you get the same cov
                  # Although, due to accumulation of numerical errors, only roughly.
                  self.assertAllClose(nngp_cov, nngp_ntk_cov)
  
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 12 for calling the PredictTest obj's underlying member method code with example usage: obj.testNTK_NTKNNGPAgreement(train_size, test_size, input_shape, network, out_logits) and returns: out, out