code: def _cov_empirical(cls, x):
      return jnp.einsum("itjk,itlk->tjl", x, x, optimize=True) / (
          x.shape[0]
          * x.shape[-1]  # pytype: disable=wrong-arg-types  # jnp-type
      )
  
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 6 for calling the PredictTest obj's underlying member method code with example usage: obj._cov_empirical(cls, x) and returns: unknown