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diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
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--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -4022,6 +4022,323 @@ Variance of the distribution.
 
 
 
+### Transformed distributions
+
+- - -
+
+### `class tf.contrib.distributions.ContinuousTransformedDistribution` {#ContinuousTransformedDistribution}
+
+A Transformed Distribution.
+
+A Transformed Distribution models `p(y)` given a base distribution `p(x)`,
+an invertible transform, `y = f(x)`, and the determinant of the Jacobian of
+`f(x)`.
+
+Shapes, type, and reparameterization are taken from the base distribution.
+
+#### Mathematical details
+
+* `p(x)` - probability distribution for random variable X
+* `p(y)` - probability distribution for random variable Y
+* `f` - transform
+* `g` - inverse transform, `f(g(x)) = x`
+* `J(x)` - Jacobian of f(x)
+
+A Transformed Distribution exposes `sample` and `pdf`:
+
+  * `sample`: `y = f(x)`, after drawing a sample of X.
+  * `pdf`: `p(y) = p(x) / det|J(x)| = p(g(y)) / det|J(g(y))|`
+
+A simple example constructing a Log-Normal distribution from a Normal
+distribution:
+
+```
+logit_normal = ContinuousTransformedDistribution(
+  base_dist=Normal(mu, sigma),
+  transform=lambda x: tf.sigmoid(x),
+  inverse=lambda y: tf.log(y) - tf.log(1. - y),
+  log_det_jacobian=(lambda x:
+      tf.reduce_sum(tf.log(tf.sigmoid(x)) + tf.log(1. - tf.sigmoid(x)),
+                    reduction_indices=[-1])))
+  name="LogitNormalTransformedDistribution"
+)
+```
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.__init__(base_dist_cls, transform, inverse, log_det_jacobian, name='ContinuousTransformedDistribution', **base_dist_args)` {#ContinuousTransformedDistribution.__init__}
+
+Construct a Transformed Distribution.
+
+##### Args:
+
+
+*  <b>`base_dist_cls`</b>: the base distribution class to transform. Must be a
+      subclass of `ContinuousDistribution`.
+*  <b>`transform`</b>: a callable that takes a `Tensor` sample from `base_dist` and
+      returns a `Tensor` of the same shape and type. `x => y`.
+*  <b>`inverse`</b>: a callable that computes the inverse of transform. `y => x`. If
+      None, users can only call `log_pdf` on values returned by `sample`.
+*  <b>`log_det_jacobian`</b>: a callable that takes a `Tensor` sample from `base_dist`
+      and returns the log of the determinant of the Jacobian of `transform`.
+*  <b>`name`</b>: The name for the distribution.
+*  <b>`**base_dist_args`</b>: kwargs to pass on to dist_cls on construction.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `base_dist_cls` is not a subclass of
+      `ContinuousDistribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.base_distribution` {#ContinuousTransformedDistribution.base_distribution}
+
+Base distribution, p(x).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.batch_shape(name='batch_shape')` {#ContinuousTransformedDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.cdf(value, name='cdf')` {#ContinuousTransformedDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.dtype` {#ContinuousTransformedDistribution.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.entropy(name='entropy')` {#ContinuousTransformedDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.event_shape(name='event_shape')` {#ContinuousTransformedDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_batch_shape()` {#ContinuousTransformedDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  batch shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_event_shape()` {#ContinuousTransformedDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  event shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.inverse` {#ContinuousTransformedDistribution.inverse}
+
+Inverse function of transform, y => x.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.is_reparameterized` {#ContinuousTransformedDistribution.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_cdf(value, name='log_cdf')` {#ContinuousTransformedDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_det_jacobian` {#ContinuousTransformedDistribution.log_det_jacobian}
+
+Function computing the log determinant of the Jacobian of transform.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_likelihood(value, name='log_likelihood')` {#ContinuousTransformedDistribution.log_likelihood}
+
+Log likelihood of this distribution (same as log_pdf).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_pdf(y, name='log_pdf')` {#ContinuousTransformedDistribution.log_pdf}
+
+Log pdf of observations in `y`.
+
+`log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+*  <b>`y`</b>: tensor of dtype `dtype`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `y`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `inverse` was not provided to the distribution and `y` was
+      not returned from `sample`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mean(name='mean')` {#ContinuousTransformedDistribution.mean}
+
+Mean of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mode(name='mode')` {#ContinuousTransformedDistribution.mode}
+
+Mode of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.name` {#ContinuousTransformedDistribution.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.pdf(y, name='pdf')` {#ContinuousTransformedDistribution.pdf}
+
+The PDF of observations in `y`.
+
+`p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+*  <b>`y`</b>: `Tensor` of dtype `dtype`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: `Tensor` of dtype `dtype`, the pdf values of `y`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.sample(n, seed=None, name='sample')` {#ContinuousTransformedDistribution.sample}
+
+Sample `n` observations.
+
+Samples from the base distribution and then passes through the transform.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.std(name='std')` {#ContinuousTransformedDistribution.std}
+
+Standard deviation of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict` {#ContinuousTransformedDistribution.strict}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict_statistics` {#ContinuousTransformedDistribution.strict_statistics}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.transform` {#ContinuousTransformedDistribution.transform}
+
+Function transforming x => y.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.variance(name='variance')` {#ContinuousTransformedDistribution.variance}
+
+Variance of the distribution.
+
+
+
+
 ## Operators allowing for matrix-free methods
 
 ### Positive definite operators