title: Putting a distribution into a tensor
body: <p>I am trying to find gradient of joint distribution with respect to mean, covarince. I have mean like </p>
  
  <pre><code>means = [tf.contrib.distributions.MultivariateNormalFullCovariance(name = 'mu_%d' % i, loc=np.zeros(2), covariance_matrix=0.1 * np.eye(2))
         for i in range(2)]
  </code></pre>
  
  <p>Now if I find the joint distribution, I have the task of calculating its gradient. To use <code>tf.gradients()</code> everything must be in the form of tensor. So I tried converting means into tensor using <code>theta_ = tf.convert_to_tensor(means)</code> but it throws error saying i</p>
  
  <blockquote>
    <p>TypeError: Failed to convert object of type  to Tensor. Contents: [<code>&lt;tf.distributions.MultivariateNormalFullCovariance 'mu_0' batch_shape=() event_shape=(2,) dtype=float64&gt;, &lt;tf.distributions.MultivariateNormalFullCovariance 'mu_1' batch_shape=() event_shape=(2,) dtype=float64&gt;</code>]. Consider casting elements to a supported type.</p>
  </blockquote>
  
  <p>Is there any other way around to calculate the gradient of distribution with respect to means, covariance?</p>