base.py 文件源码

def sample(self, n_samples=None):
        """
        sample(n_samples=None)

        Return samples from the distribution. When `n_samples` is None (by
        default), one sample of shape ``batch_shape + value_shape`` is
        generated. For a scalar `n_samples`, the returned Tensor has a new
        sample dimension with size `n_samples` inserted at ``axis=0``, i.e.,
        the shape of samples is ``[n_samples] + batch_shape + value_shape``.

        :param n_samples: A 0-D `int32` Tensor or None. How many independent
            samples to draw from the distribution.
        :return: A Tensor of samples.
        """
        if n_samples is None:
            samples = self._sample(n_samples=1)
            return tf.squeeze(samples, axis=0)
        elif isinstance(n_samples, int):
            return self._sample(n_samples)
        else:
            n_samples = tf.convert_to_tensor(n_samples, dtype=tf.int32)
            _assert_rank_op = tf.assert_rank(
                n_samples, 0,
                message="n_samples should be a scalar (0-D Tensor).")
            with tf.control_dependencies([_assert_rank_op]):
                samples = self._sample(n_samples)
            return samples