Update generated Python Op docs.

Change: 127885597

18 files changed, 878 insertions, 58 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index c5adf4a34a..ec6d739a7b 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -39,15 +39,16 @@ All distributions support batches of independent distributions of that type.
 The batch shape is determined by broadcasting together the parameters.
 
 The shape of arguments to `__init__`, `cdf`, `log_cdf`, `prob`, and
-`log_prob` reflect this broadcasting, as does the return value of `sample`.
+`log_prob` reflect this broadcasting, as does the return value of `sample` and
+`sample_n`.
 
-`sample_shape = (n,) + batch_shape + event_shape`, where `sample_shape` is the
-shape of the `Tensor` returned from `sample`, `n` is the number of samples,
-`batch_shape` defines how many independent distributions there are, and
-`event_shape` defines the shape of samples from each of those independent
-distributions. Samples are independent along the `batch_shape` dimensions,
-but not necessarily so along the `event_shape` dimensions (dependending on
-the particulars of the underlying distribution).
+`sample_n_shape = (n,) + batch_shape + event_shape`, where `sample_n_shape` is
+the shape of the `Tensor` returned from `sample_n`, `n` is the number of
+samples, `batch_shape` defines how many independent distributions there are,
+and `event_shape` defines the shape of samples from each of those independent
+distributions. Samples are independent along the `batch_shape` dimensions, but
+not necessarily so along the `event_shape` dimensions (dependending on the
+particulars of the underlying distribution).
 
 Using the `Uniform` distribution as an example:
 
@@ -69,7 +70,7 @@ event_shape_t = u.event_shape
 # Sampling returns a sample per distribution.  `samples` has shape
 # (5, 2, 2), which is (n,) + batch_shape + event_shape, where n=5,
 # batch_shape=(2, 2), and event_shape=().
-samples = u.sample(5)
+samples = u.sample_n(5)
 
 # The broadcasting holds across methods. Here we use `cdf` as an example. The
 # same holds for `log_cdf` and the likelihood functions.
@@ -277,7 +278,31 @@ Probability density/mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Distribution.sample(n, seed=None, name='sample')` {#Distribution.sample}
+#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n')` {#Distribution.sample_n}
 
 Generate `n` samples.
 
@@ -578,7 +603,31 @@ Probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.sample(n, seed=None, name='sample')` {#Bernoulli.sample}
+#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n')` {#Bernoulli.sample_n}
 
 Generate `n` samples.
 
@@ -970,7 +1019,31 @@ The probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Beta.sample(n, seed=None, name='sample')` {#Beta.sample}
+#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n}
 
 Sample `n` observations from the Beta Distributions.
 
@@ -1145,7 +1218,8 @@ Log-probability of class `k`.
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor with shape = `self.batch_shape()`.
+*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with a `batch_shape`
+    `Tensor`.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -1211,7 +1285,7 @@ Probability of class `k`.
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor with shape = `self.batch_shape()`.
+*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with logits.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -1221,7 +1295,31 @@ Probability of class `k`.
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.sample(n, seed=None, name='sample')` {#Categorical.sample}
+#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n}
 
 Sample `n` observations from the Categorical distribution.
 
@@ -1574,7 +1672,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.sample(n, seed=None, name='sample')` {#Chi2.sample}
+#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n}
 
 Draws `n` samples from the Gamma distribution(s).
 
@@ -1932,7 +2054,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.sample(n, seed=None, name=None)` {#Exponential.sample}
+#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n}
 
 Sample `n` observations from the Exponential Distributions.
 
@@ -2304,7 +2450,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.sample(n, seed=None, name='sample')` {#Gamma.sample}
+#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n}
 
 Draws `n` samples from the Gamma distribution(s).
 
@@ -2689,7 +2859,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.sample(n, seed=None, name='sample')` {#InverseGamma.sample}
+#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n}
 
 Draws `n` samples from these InverseGamma distribution(s).
 
@@ -3032,7 +3226,31 @@ The prob of observations in `x` under the Laplace distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.sample(n, seed=None, name='sample')` {#Laplace.sample}
+#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n}
 
 Sample `n` observations from the Laplace Distributions.
 
@@ -3390,7 +3608,31 @@ The PDF of observations in `x` under these Normal distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Normal.sample(n, seed=None, name='sample')` {#Normal.sample}
+#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n}
 
 Sample `n` observations from the Normal Distributions.
 
@@ -3722,7 +3964,31 @@ The PDF of observations in `x` under these Student's t distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.sample(n, seed=None, name='sample')` {#StudentT.sample}
+#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n}
 
 Sample `n` observations from the Student t Distributions.
 
@@ -4040,7 +4306,31 @@ The PDF of observations in `x` under these Uniform distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.sample(n, seed=None, name='sample')` {#Uniform.sample}
+#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n}
 
 Sample `n` observations from the Uniform Distributions.
 
@@ -4365,7 +4655,31 @@ OR
 
 - - -
 
-#### `tf.contrib.distributions.MultivariateNormalFull.sample(n, seed=None, name='sample')` {#MultivariateNormalFull.sample}
+#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n}
 
 Sample `n` observations from the Multivariate Normal Distributions.
 
@@ -4704,7 +5018,31 @@ OR
 
 - - -
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(n, seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
+#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n}
 
 Sample `n` observations from the Multivariate Normal Distributions.
 
@@ -5138,9 +5476,33 @@ The probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.sample(n, seed=None, name='sample')` {#Dirichlet.sample}
+#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample}
 
-Sample `n` observations from the Normal Distributions.
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n}
+
+Sample `n` observations from the distributions.
 
 ##### Args:
 
@@ -5523,7 +5885,31 @@ probability includes a combinatorial coefficient.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.sample(n, seed=None, name='sample')` {#DirichletMultinomial.sample}
+#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')` {#DirichletMultinomial.sample_n}
 
 Generate `n` samples.
 
@@ -5860,7 +6246,31 @@ The prob of observations in `y`.
 
 - - -
 
-#### `tf.contrib.distributions.TransformedDistribution.sample(n, seed=None, name='sample')` {#TransformedDistribution.sample}
+#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n}
 
 Sample `n` observations.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
index a85613e709..0162e518d3 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
@@ -245,7 +245,31 @@ Probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.sample(n, seed=None, name='sample')` {#Bernoulli.sample}
+#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n')` {#Bernoulli.sample_n}
 
 Generate `n` samples.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
index babf301454..1fe3d6f56b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
@@ -308,9 +308,33 @@ The probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.sample(n, seed=None, name='sample')` {#Dirichlet.sample}
+#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample}
 
-Sample `n` observations from the Normal Distributions.
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n}
+
+Sample `n` observations from the distributions.
 
 ##### Args:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
index b4cc82ddc2..27559a8e4d 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
@@ -21,15 +21,16 @@ All distributions support batches of independent distributions of that type.
 The batch shape is determined by broadcasting together the parameters.
 
 The shape of arguments to `__init__`, `cdf`, `log_cdf`, `prob`, and
-`log_prob` reflect this broadcasting, as does the return value of `sample`.
+`log_prob` reflect this broadcasting, as does the return value of `sample` and
+`sample_n`.
 
-`sample_shape = (n,) + batch_shape + event_shape`, where `sample_shape` is the
-shape of the `Tensor` returned from `sample`, `n` is the number of samples,
-`batch_shape` defines how many independent distributions there are, and
-`event_shape` defines the shape of samples from each of those independent
-distributions. Samples are independent along the `batch_shape` dimensions,
-but not necessarily so along the `event_shape` dimensions (dependending on
-the particulars of the underlying distribution).
+`sample_n_shape = (n,) + batch_shape + event_shape`, where `sample_n_shape` is
+the shape of the `Tensor` returned from `sample_n`, `n` is the number of
+samples, `batch_shape` defines how many independent distributions there are,
+and `event_shape` defines the shape of samples from each of those independent
+distributions. Samples are independent along the `batch_shape` dimensions, but
+not necessarily so along the `event_shape` dimensions (dependending on the
+particulars of the underlying distribution).
 
 Using the `Uniform` distribution as an example:
 
@@ -51,7 +52,7 @@ event_shape_t = u.event_shape
 # Sampling returns a sample per distribution.  `samples` has shape
 # (5, 2, 2), which is (n,) + batch_shape + event_shape, where n=5,
 # batch_shape=(2, 2), and event_shape=().
-samples = u.sample(5)
+samples = u.sample_n(5)
 
 # The broadcasting holds across methods. Here we use `cdf` as an example. The
 # same holds for `log_cdf` and the likelihood functions.
@@ -259,7 +260,31 @@ Probability density/mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Distribution.sample(n, seed=None, name='sample')` {#Distribution.sample}
+#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n')` {#Distribution.sample_n}
 
 Generate `n` samples.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
index e3745642c3..f93ae572dd 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
@@ -272,7 +272,31 @@ OR
 
 - - -
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(n, seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
+#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n}
 
 Sample `n` observations from the Multivariate Normal Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
index c5c22bb471..6120886ce4 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
@@ -272,7 +272,31 @@ The PDF of observations in `x` under these Student's t distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.sample(n, seed=None, name='sample')` {#StudentT.sample}
+#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n}
 
 Sample `n` observations from the Student t Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
index 589801ca51..5d11a754be 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
@@ -281,7 +281,31 @@ The prob of observations in `y`.
 
 - - -
 
-#### `tf.contrib.distributions.TransformedDistribution.sample(n, seed=None, name='sample')` {#TransformedDistribution.sample}
+#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n}
 
 Sample `n` observations.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
index e0dcfe5e27..a7e027473c 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
@@ -122,7 +122,8 @@ Log-probability of class `k`.
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor with shape = `self.batch_shape()`.
+*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with a `batch_shape`
+    `Tensor`.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -188,7 +189,7 @@ Probability of class `k`.
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor with shape = `self.batch_shape()`.
+*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with logits.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -198,7 +199,31 @@ Probability of class `k`.
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.sample(n, seed=None, name='sample')` {#Categorical.sample}
+#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n}
 
 Sample `n` observations from the Categorical distribution.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
index 8c71f58aa1..b1c9f857e6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
@@ -302,7 +302,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.sample(n, seed=None, name='sample')` {#Chi2.sample}
+#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n}
 
 Draws `n` samples from the Gamma distribution(s).
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
index b4e68fc33a..dc74a688ea 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
@@ -237,7 +237,31 @@ The PDF of observations in `x` under these Uniform distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.sample(n, seed=None, name='sample')` {#Uniform.sample}
+#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n}
 
 Sample `n` observations from the Uniform Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
index c844b5b122..56a82357bc 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
@@ -332,7 +332,31 @@ probability includes a combinatorial coefficient.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.sample(n, seed=None, name='sample')` {#DirichletMultinomial.sample}
+#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')` {#DirichletMultinomial.sample_n}
 
 Generate `n` samples.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
index 63302451fb..565a007f2e 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
@@ -302,7 +302,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.sample(n, seed=None, name=None)` {#Exponential.sample}
+#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n}
 
 Sample `n` observations from the Exponential Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
index 55d4c391a0..8d03608c93 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
@@ -319,7 +319,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.sample(n, seed=None, name='sample')` {#Gamma.sample}
+#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n}
 
 Draws `n` samples from the Gamma distribution(s).
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
index f3acefc288..68e28c3253 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
@@ -329,7 +329,31 @@ Pdf of observations in `x` under these Gamma distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.sample(n, seed=None, name='sample')` {#InverseGamma.sample}
+#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n}
 
 Draws `n` samples from these InverseGamma distribution(s).
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
index 50291c4792..d866384032 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
@@ -319,7 +319,31 @@ The probability mass function.
 
 - - -
 
-#### `tf.contrib.distributions.Beta.sample(n, seed=None, name='sample')` {#Beta.sample}
+#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n}
 
 Sample `n` observations from the Beta Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
index 89179d0e1a..1e1953caa3 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
@@ -274,7 +274,31 @@ The prob of observations in `x` under the Laplace distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.sample(n, seed=None, name='sample')` {#Laplace.sample}
+#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n}
 
 Sample `n` observations from the Laplace Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
index 535e8ca99e..706654c65d 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
@@ -267,7 +267,31 @@ OR
 
 - - -
 
-#### `tf.contrib.distributions.MultivariateNormalFull.sample(n, seed=None, name='sample')` {#MultivariateNormalFull.sample}
+#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n}
 
 Sample `n` observations from the Multivariate Normal Distributions.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
index f0dc36e771..7370b72a8c 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
@@ -298,7 +298,31 @@ The PDF of observations in `x` under these Normal distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Normal.sample(n, seed=None, name='sample')` {#Normal.sample}
+#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample}
+
+Generate samples of the specified shape for each batched distribution.
+
+Note that a call to `sample()` without arguments will generate a single
+sample per batched distribution.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `int32` `Tensor` or tuple or list. Shape of the generated
+    samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
+      `sample_shape + self.batch_shape + self.event_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n}
 
 Sample `n` observations from the Normal Distributions.