code: def testGradientDescentMseEnsembleGet(
      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
      )
      _, _, kernel_fn = _build_network(
          train_shape[1:], network, out_logits
      )
  
      predictor = predict.gradient_descent_mse_ensemble(
          kernel_fn, x_train, y_train, diag_reg=0.0
      )
      for x in [None, "x_test"]:
          with self.subTest(x=x):
              x = x if x is None else x_test
              out = predictor(None, x, "ntk", compute_cov=True)
              assert isinstance(out, predict.Gaussian)
  
              out = predictor(1.0, x, "nngp", compute_cov=True)
              assert isinstance(out, predict.Gaussian)
  
              out = predictor(
                  jnp.array([0.0, 1.0]), x, ("ntk",), compute_cov=True
              )
              assert len(out) == 1 and isinstance(
                  out[0], predict.Gaussian
              )
  
              out = predictor(2.0, x, ("ntk", "nngp"), compute_cov=True)
              assert (
                  len(out) == 2
                  and isinstance(out[0], predict.Gaussian)
                  and isinstance(out[1], predict.Gaussian)
              )
  
              out2 = predictor(
                  2.0, x, ("nngp", "ntk"), compute_cov=True
              )
              self.assertAllClose(out[0], out2[1])
              self.assertAllClose(out[1], out2[0])
  
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 8 for calling the PredictTest obj's underlying member method code with example usage: obj.testGradientDescentMseEnsembleGet(train_size, test_size, input_shape, network, out_logits) without return types