univariate.py 文件源码

def _sample(self, n_samples):
        mean, std = self.mean, self.std
        if not self.is_reparameterized:
            mean = tf.stop_gradient(mean)
            std = tf.stop_gradient(std)
        shape = tf.concat([[n_samples], self.batch_shape], 0)
        samples = tf.random_normal(shape, dtype=self.dtype) * std + mean
        static_n_samples = n_samples if isinstance(n_samples, int) else None
        samples.set_shape(
            tf.TensorShape([static_n_samples]).concatenate(
                self.get_batch_shape()))
        return samples