code: def testNTKGDPrediction(
      self,
      train_size,
      test_size,
      input_shape,
      network,
      out_logits,
      kernel_type,
      momentum,
      learning_rate,
      t,
      loss,
  ):
      train_shape = (train_size, *input_shape)
      test_shape = (test_size, *input_shape)
      key, x_test, x_train, y_train = self._get_inputs(
          out_logits, test_shape, train_shape
      )
  
      fn_and_kernel = KERNELS[kernel_type]
      params, f, ntk = fn_and_kernel(
          key, train_shape[1:], network, out_logits
      )
  
      g_dd = ntk(x_train, None, "ntk")
      g_td = ntk(x_test, x_train, "ntk")
  
      # Regress to an MSE loss.
      loss_fn = lambda y, y_hat: 0.5 * jnp.mean((y - y_hat) ** 2)
      grad_loss = jit(
          grad(lambda params, x: loss_fn(f(params, x), y_train))
      )
  
      trace_axes = () if g_dd.ndim == 4 else (-1,)
      if loss == "mse_analytic":
          if momentum is not None:
              raise absltest.SkipTest(momentum)
          predictor = predict.gradient_descent_mse(
              g_dd,
              y_train,
              learning_rate=learning_rate,
              trace_axes=trace_axes,
          )
      elif loss == "mse":
          predictor = predict.gradient_descent(
              loss_fn,
              g_dd,
              y_train,
              learning_rate=learning_rate,
              momentum=momentum,
              trace_axes=trace_axes,
          )
      else:
          raise NotImplementedError(loss)
  
      predictor = jit(predictor)
  
      fx_train_0 = f(params, x_train)
      fx_test_0 = f(params, x_test)
  
      self._test_zero_time(
          predictor, fx_train_0, fx_test_0, g_td, momentum
      )
      self._test_multi_step(
          predictor, fx_train_0, fx_test_0, g_td, momentum
      )
      if loss == "mse_analytic":
          self._test_inf_time(
              predictor, fx_train_0, fx_test_0, g_td, y_train
          )
  
      if momentum is None:
          opt_init, opt_update, get_params = optimizers.sgd(
              learning_rate
          )
      else:
          opt_init, opt_update, get_params = optimizers.momentum(
              learning_rate, momentum
          )
  
      opt_state = opt_init(params)
      for i in range(t):
          params = get_params(opt_state)
          opt_state = opt_update(
              i, grad_loss(params, x_train), opt_state
          )
  
      params = get_params(opt_state)
  
      fx_train_nn, fx_test_nn = f(params, x_train), f(params, x_test)
      fx_train_t, fx_test_t = predictor(t, fx_train_0, fx_test_0, g_td)
  
      self.assertAllClose(fx_train_nn, fx_train_t, rtol=RTOL, atol=ATOL)
      self.assertAllClose(fx_test_nn, fx_test_t, rtol=RTOL, atol=ATOL)
  
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 5 for calling the PredictTest obj's underlying member method code with example usage: obj.testNTKGDPrediction(train_size, test_size, input_shape, network, out_logits, kernel_type, momentum, learning_rate, t, loss) without return types