code: def testPredCovPosDef(
      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
      )
  
      ts = jnp.logspace(-3, 3, 10)
      predict_fn_mse_ens = predict.gradient_descent_mse_ensemble(
          ker_fun, x_train, y_train
      )
  
      for get in ("nngp", "ntk"):
          for x in (None, "x_test"):
              for t in (None, "ts"):
                  with self.subTest(get=get, x=x, t=t):
                      cov = predict_fn_mse_ens(
                          t=t if t is None else ts,
                          get=get,
                          x_test=x if x is None else x_test,
                          compute_cov=True,
                      ).covariance
  
                      self.assertAllClose(
                          cov, jnp.moveaxis(cov, -1, -2)
                      )
                      self.assertGreater(
                          jnp.min(jnp.linalg.eigh(cov)[0]), -1e-4
                      )
  
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 13 for calling the PredictTest obj's underlying member method code with example usage: obj.testPredCovPosDef(train_size, test_size, input_shape, network, out_logits) without return types