code: def testGradientDescentMseEnsembleTrain(self):
      key = random.PRNGKey(1)
      x = random.normal(key, (8, 4, 6, 3))
      _, _, kernel_fn = stax.serial(
          stax.Conv(1, (2, 2)), stax.Relu(), stax.Conv(1, (2, 1))
      )
      y = random.normal(key, (8, 2, 5, 1))
      predictor = predict.gradient_descent_mse_ensemble(
          kernel_fn, x, y, diagonal_spatial=False
      )
  
      for t in [None, jnp.array([0.0, 1.0, 10.0])]:
          with self.subTest(t=t):
              y_none = predictor(t, None, None, compute_cov=True)
              y_x = predictor(t, x, None, compute_cov=True)
              self.assertAllClose(y_none, y_x, rtol=0.04, atol=0.04)
  
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 15 for calling the PredictTest obj's underlying member method code with example usage: obj.testGradientDescentMseEnsembleTrain() without return types