how can i use tfp.math.psd_kernels.Polynomial, with feature_ndims=6,

提供された例を使って、報告された shape エラーを再現します。tfp.math.psd_kernels.Polynomial と tfp.layers.VariationalGaussianProcess の API を読み、feature_ndims、event_shape、inducing_index_points_initializer に注意してください。互換性のない shape の想定を特定し、再現可能な修正またはライブラリのバグであることの確認を文書化できれば完了です。

with the following code how can i use tfp.math.psd_kernels.Polynomial, with feature_ndims=6, i ussually get an error , please help.

code =
class PolynomialKernelFn(tf_keras.layers.Layer):
def init(self, bias_amplitude=0.0, slope_amplitude=1.0, shift=0.0, exponent=3.0, feature_ndims=1, **kwargs):
super(PolynomialKernelFn, self).init(**kwargs)
dtype = kwargs.get('dtype', None)
self.jitter = 1e-4; # Adding a small jitter for numerical stability
def call(self, inputs): return self.kernel;

@property     
def kernel(self):    
    return tfp.math.psd_kernels.Polynomial( bias_amplitude=0.0, slope_amplitude=1.0, shift=0.0, exponent=3.0, feature_ndims=6, );    

//end_class

//Example data
x_train = np.random.uniform(-3., 3., size=(100, 6));
y_train = np.sin(x_train[:, 0:2]); y_train = x_train;
print( 'x_train', x_train.shape, 'y_train', y_train.shape, );

x_range = [np.min(x_train), np.max(x_train)];
num_inducing_points = 18;

induc_idx_points = np.random.uniform(-3.0, 3.0, size=(num_inducing_points, x_train.shape[1]))
induc_idx_point_init = tf.constant_initializer(induc_idx_points)
print('induc_idx_points shape:', induc_idx_points.shape)

noise0 = ( tf.constant_initializer(np.array(0.54).astype(x_train.dtype)) );
// noise0 = ( tf.constant_initializer(np.array([ [0.54] ]).astype(x_train.dtype)) );
print( 'noise0', noise0.value.shape, );

polynomial_kernel = tfp.math.psd_kernels.Polynomial(
bias_amplitude=1.0, slope_amplitude=1.0, shift=0.0, exponent=2, feature_ndims=6, validate_args=True );

// Build model
model = tf_keras.Sequential([
tf_keras.layers.InputLayer( input_shape=(x_train.shape[1],) ),

tf_keras.layers.Dense(1, kernel_initializer='ones', use_bias=False), 
tfp.layers.VariationalGaussianProcess(
    num_inducing_points= num_inducing_points,
    kernel_provider= PolynomialKernelFn(),
    event_shape= [1],
    inducing_index_points_initializer= induc_idx_point_init, 
    unconstrained_observation_noise_variance_initializer= noise0,
),  

]);
model.summary();

// Compile the model
model.compile(optimizer=tf_keras.optimizers.Adam(), loss=lambda y, p_y: -p_y.log_prob(y), );
model.fit(x_train, y_train, epochs=3, );

// Predict
x_test = np.linspace(-3., 3., 30).reshape(-1, 6);
y_pred = model.predict(x_test);

error = TypeError: Eager execution of tf.constant with unsupported shape. Tensor [[ 2.6181169 -0.63000023 0.88848853 2.8256464 0.7070044 0.10845122]
[ 1.7892019 0.9782654 0.7722825 -1.3020381 -2.2474375 -2.7869945 ]
[-1.7044429 1.3536608 1.6787233 -1.0234025 -0.56131506 -1.3617553 ]
[-0.67438734 -2.781567 -2.8270533 1.1969172 0.6445283 -2.27967 ]
[ 2.360385 -0.04742741 0.59732205 -2.933202 0.7861253 2.8390534 ]
[ 2.8995507 -2.7624567 1.4198784 2.6410117 -1.4379357 -1.7750715 ]
[ 1.152713 -1.1086357 -1.0503062 -1.4047132 2.0060854 -1.1540136 ]
[-2.5986419 0.97180235 1.4190216 -1.3796597 0.29559672 -1.4235702 ]
[ 2.4803004 2.498159 -0.7950366 1.8512287 2.291289 -0.8069504 ]
[ 2.9575715 1.8804133 2.8104806 -1.9198297 -1.3981376 2.7783432 ]
[ 0.5030356 -1.099595 0.3222227 0.6970968 -0.53885055 -0.7117024 ]
[ 2.3374352 -1.5795385 2.6345415 0.8455149 2.956886 0.8089901 ]
[ 2.72245 -0.10884786 -0.5315385 -0.4300995 -1.4305451 0.99511087]
[ 1.7424549 2.3463657 2.444201 0.5814336 -0.8842765 0.9178352 ]
[ 2.8574386 2.663537 1.0705398 0.31068817 0.22017056 1.424388 ]
[ 1.5786017 1.0860821 -2.569758 -0.1683704 -0.87796944 2.7431 ]
[-2.030426 -2.789794 -1.2321734 -0.4800363 1.2280728 2.926302 ]
[ 1.3148888 2.8946493 2.2137816 -1.0343827 1.1032671 -0.5407205 ]] (converted from [[ 2.61811684 -0.63000021 0.88848855 2.82564638 0.70700445 0.10845122]
[ 1.78920188 0.97826541 0.77228246 -1.30203812 -2.24743741 -2.78699451]
[-1.70444284 1.35366083 1.67872335 -1.02340239 -0.56131508 -1.36175523]
[-0.67438734 -2.78156708 -2.82705339 1.19691717 0.64452832 -2.27967009]
[ 2.36038489 -0.04742741 0.59732205 -2.93320213 0.78612532 2.83905331]
[ 2.89955057 -2.76245673 1.41987839 2.64101165 -1.43793566 -1.77507154]
[ 1.15271291 -1.10863564 -1.05030615 -1.40471315 2.00608531 -1.15401369]
[-2.59864192 0.97180237 1.41902165 -1.37965962 0.29559672 -1.4235702 ]
[ 2.48030032 2.49815891 -0.79503662 1.85122868 2.291289 -0.80695038]
[ 2.95757162 1.88041332 2.81048062 -1.91982973 -1.39813751 2.77834323]
[ 0.50303558 -1.09959492 0.3222227 0.69709683 -0.53885057 -0.71170238]
[ 2.33743521 -1.57953841 2.63454156 0.84551489 2.95688615 0.80899011]
[ 2.72245013 -0.10884786 -0.53153849 -0.43009949 -1.43054515 0.99511088]
[ 1.74245491 2.3463657 2.44420105 0.58143359 -0.88427652 0.91783519]
[ 2.85743859 2.66353695 1.0705398 0.31068815 0.22017056 1.42438809]
[ 1.5786017 1.08608213 -2.56975798 -0.16837039 -0.87796944 2.74310004]
[-2.03042604 -2.78979406 -1.23217341 -0.48003628 1.22807274 2.92630186]
[ 1.31488889 2.89464926 2.21378162 -1.03438269 1.10326708 -0.5407205 ]]) has 108 elements, but got shape (1, 18, None, 1) with None elements).