我正在尝试在Linux主机上使用dex2oat将APK（更具体地说是dex文件）编译为OAT。我按照给出的here的说明进行操作，并出现以下错误：
@params_as_tensors
def precompute(self):
p_num_inducing = len(self.feature)
p_err = self.Y - self.mean_function(self.X)
p_Kuf = self.feature.Kuf(self.kern, self.X)
p_Kuu = self.feature.Kuu(self.kern, jitter=settings.numerics.jitter_level)
p_sigma = tf.sqrt(self.likelihood.variance)
self.p_L = tf.cholesky(p_Kuu)
p_A = tf.matrix_triangular_solve(self.p_L, p_Kuf, lower=True) / p_sigma
p_B = tf.matmul(p_A, p_A, transpose_b=True) + tf.eye(p_num_inducing, dtype=settings.tf_float)
self.p_LB = tf.cholesky(p_B)
p_Aerr = tf.matmul(p_A, p_err)
self.p_c = tf.matrix_triangular_solve(self.p_LB, p_Aerr, lower=True) / p_sigma
@params_as_tensors
def _build_predict(self, Xnew, full_cov=False):
"""
Compute the mean and variance of the latent function at some new points
Xnew. For a derivation of the terms in here, see the associated SGPR
notebook.
"""
Kus = self.feature.Kuf(self.kern, Xnew)
tmp1 = tf.matrix_triangular_solve(self.p_L, Kus, lower=True)
tmp2 = tf.matrix_triangular_solve(self.p_LB, tmp1, lower=True)
mean = tf.matmul(tmp2, self.p_c, transpose_a=True)
if full_cov:
var = self.kern.K(Xnew) + tf.matmul(tmp2, tmp2, transpose_a=True) \
- tf.matmul(tmp1, tmp1, transpose_a=True)
shape = tf.stack([1, 1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 2), shape)
else:
var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0) \
- tf.reduce_sum(tf.square(tmp1), 0)
shape = tf.stack([1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 1), shape)
return mean + self.mean_function(Xnew), var
它会响起钟声吗，还是有另一种无需使用仿真器或VM即可编译为OAT的方法？