message: Move a couple more docs from tf.contrib -> tfp
diff: @@ -524,6 +524,8 @@ def matrix_diag_transform(matrix, transform=None, name=None):
  Example of heteroskedastic 2-D linear regression.
  ```python
  + tfd = tfp.distributions
  +
  # Get a trainable Cholesky factor.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
  @@ -533,7 +535,7 @@ def matrix_diag_transform(matrix, transform=None, name=None):
  mu = tf.contrib.layers.fully_connected(activations, 2)
  # This is a fully trainable multivariate normal!
  - dist = tf.contrib.distributions.MVNCholesky(mu, chol)
  + dist = tfd.MultivariateNormalTriL(mu, chol)
  # Standard log loss. Minimizing this will "train" mu and chol, and then dist
  # will be a distribution predicting labels as multivariate Gaussians.
  