Update generated Python Op docs.

Change: 123716704

2 files changed, 288 insertions, 68 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index c38a71eaaa..d162cde5f1 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -2322,7 +2322,7 @@ Sample `n` observations from the Multivariate Normal Distributions.
 DirichletMultinomial mixture distribution.
 
 This distribution is parameterized by a vector `alpha` of concentration
-parameters for `k` classes.
+parameters for `k` classes and `n`, the counts per each class..
 
 #### Mathematical details
 
@@ -2332,21 +2332,21 @@ probability of these draws being made from the distribution.  The distribution
 has hyperparameters `alpha = (alpha_1,...,alpha_k)`, and probability mass
 function (pmf):
 
-```pmf(counts) = C! / (c_1!...c_k!) * Beta(alpha + c) / Beta(alpha)```
+```pmf(counts) = N! / (n_1!...n_k!) * Beta(alpha + c) / Beta(alpha)```
 
-where above `C = sum_j c_j`, `N!` is `N` factorial, and
+where above `N = sum_j n_j`, `N!` is `N` factorial, and
 `Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the multivariate beta
 function.
 
-This is a mixture distribution in that `N` samples can be produced by:
+This is a mixture distribution in that `M` samples can be produced by:
   1. Choose class probabilities `p = (p_1,...,p_k) ~ Dir(alpha)`
-  2. Draw integers `m = (m_1,...,m_k) ~ Multinomial(p, N)`
+  2. Draw integers `m = (n_1,...,n_k) ~ Multinomial(N, p)`
 
 This class provides methods to create indexed batches of Dirichlet
 Multinomial distributions.  If the provided `alpha` is rank 2 or higher, for
 every fixed set of leading dimensions, the last dimension represents one
 single Dirichlet Multinomial distribution.  When calling distribution
-functions (e.g. `dist.pdf(counts)`), `alpha` and `counts` are broadcast to the
+functions (e.g. `dist.pmf(counts)`), `alpha` and `counts` are broadcast to the
 same shape (if possible).  In all cases, the last dimension of alpha/counts
 represents single Dirichlet Multinomial distributions.
 
@@ -2354,7 +2354,8 @@ represents single Dirichlet Multinomial distributions.
 
 ```python
 alpha = [1, 2, 3]
-dist = DirichletMultinomial(alpha)
+n = 2
+dist = DirichletMultinomial(n, alpha)
 ```
 
 Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
@@ -2362,40 +2363,50 @@ The distribution functions can be evaluated on counts.
 
 ```python
 # counts same shape as alpha.
-counts = [0, 2, 0]
-dist.pdf(counts)  # Shape []
+counts = [0, 0, 2]
+dist.pmf(counts)  # Shape []
 
 # alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts.
-counts = [[11, 22, 33], [44, 55, 66]]
-dist.pdf(counts)  # Shape [2]
+counts = [[1, 1, 0], [1, 0, 1]]
+dist.pmf(counts)  # Shape [2]
 
 # alpha will be broadcast to shape [5, 7, 3] to match counts.
 counts = [[...]]  # Shape [5, 7, 3]
-dist.pdf(counts)  # Shape [5, 7]
+dist.pmf(counts)  # Shape [5, 7]
 ```
 
 Creates a 2-batch of 3-class distributions.
 
 ```python
 alpha = [[1, 2, 3], [4, 5, 6]]  # Shape [2, 3]
-dist = DirichletMultinomial(alpha)
+n = [3, 3]
+dist = DirichletMultinomial(n, alpha)
 
-# counts will be broadcast to [[11, 22, 33], [11, 22, 33]] to match alpha.
-counts = [11, 22, 33]
-dist.pdf(counts)  # Shape [2]
+# counts will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha.
+counts = [2, 1, 0]
+dist.pmf(counts)  # Shape [2]
 ```
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.__init__(alpha)` {#DirichletMultinomial.__init__}
+#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, name='DirichletMultinomial', allow_arbitrary_counts=False)` {#DirichletMultinomial.__init__}
 
 Initialize a batch of DirichletMultinomial distributions.
 
 ##### Args:
 
 
-*  <b>`alpha`</b>: Shape `[N1,..., Nn, k]` positive `float` or `double` tensor with
-    `n >= 0`.  Defines this as a batch of `N1 x ... x Nn` different `k`
-    class Dirichlet multinomial distributions.
+*  <b>`n`</b>: Non-negative `float` or `double` tensor with shape
+    broadcastable to `[N1,..., Nm]` with `m >= 0`.  Defines this as a batch
+    of `N1 x ... x Nm` different Dirichlet multinomial distributions. Its
+    components should be equal to integral values.
+*  <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to
+    `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
+     different `k` class Dirichlet multinomial distributions.
+*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
+*  <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf
+    allows for the `counts` tensor to be non-integral values.
+    The pmf/cdf are functions that can be evaluated at non-integral values,
+    but are only a distribution over non-negative integers.
 
 
 *  <b>`Examples`</b>: 
@@ -2403,10 +2414,10 @@ Initialize a batch of DirichletMultinomial distributions.
 ```python
 # Define 1-batch of 2-class Dirichlet multinomial distribution,
 # also known as a beta-binomial.
-dist = DirichletMultinomial([1.1, 2.0])
+dist = DirichletMultinomial(2.0, [1.1, 2.0])
 
 # Define a 2-batch of 3-class distributions.
-dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 ```
 
 
@@ -2414,7 +2425,26 @@ dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha}
 
-Parameters defining this distribution.
+Parameter defining this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.batch_shape(name='batch_shape')` {#DirichletMultinomial.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`
 
 
 - - -
@@ -2428,7 +2458,56 @@ Parameters defining this distribution.
 
 #### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
 
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.entropy(name=None)` {#DirichletMultinomial.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.event_shape(name='event_shape')` {#DirichletMultinomial.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.DirichletMultinomial.get_batch_shape()` {#DirichletMultinomial.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.DirichletMultinomial.get_event_shape()` {#DirichletMultinomial.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  event shape
 
 
 - - -
@@ -2440,23 +2519,25 @@ Parameters defining this distribution.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name=None)` {#DirichletMultinomial.log_pmf}
+#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name='log_pmf')` {#DirichletMultinomial.log_pmf}
 
 `Log(P[counts])`, computed for every batch member.
 
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
+For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
+that after sampling `n` draws from this Dirichlet Multinomial
+distribution, the number of draws falling in class `j` is `n_j`.  Note that
 different sequences of draws can result in the same counts, thus the
 probability includes a combinatorial coefficient.
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+*  <b>`counts`</b>: Non-negative `float` or `double` tensor whose shape can
     be broadcast with `self.alpha`.  For fixed leading dimensions, the last
     dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
+    distribution in `self.alpha`. `counts` is only legal if it sums up to
+    `n` and its components are equal to integral values. The second
+    condition is relaxed if `allow_arbitrary_counts` is set.
 *  <b>`name`</b>: Name to give this Op, defaults to "log_pmf".
 
 ##### Returns:
@@ -2473,14 +2554,21 @@ Class means for every batch member.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.num_classes` {#DirichletMultinomial.num_classes}
+#### `tf.contrib.distributions.DirichletMultinomial.n` {#DirichletMultinomial.n}
+
+Parameter defining this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.name` {#DirichletMultinomial.name}
 
-Tensor providing number of classes in each batch member.
+Name to prepend to all ops.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name=None)` {#DirichletMultinomial.pmf}
+#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name='pmf')` {#DirichletMultinomial.pmf}
 
 `P[counts]`, computed for every batch member.
 
@@ -2493,10 +2581,12 @@ probability includes a combinatorial coefficient.
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+*  <b>`counts`</b>: Non-negative `float`, `double` tensor whose shape can
     be broadcast with `self.alpha`.  For fixed leading dimensions, the last
     dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
+    distribution in `self.alpha`. `counts` is only legal if it sums up to
+    `n` and its components are equal to integral values. The second
+    condition is relaxed if `allow_arbitrary_counts` is set.
 *  <b>`name`</b>: Name to give this Op, defaults to "pmf".
 
 ##### Returns:
@@ -2504,6 +2594,26 @@ probability includes a combinatorial coefficient.
   Probabilities for each record, shape `[N1,...,Nn]`.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.sample(n, seed=None, name=None)` {#DirichletMultinomial.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
 
 
 ## Posterior inference with conjugate priors.
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 1d8cb6a6dd..31b1382ed8 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
@@ -1,7 +1,7 @@
 DirichletMultinomial mixture distribution.
 
 This distribution is parameterized by a vector `alpha` of concentration
-parameters for `k` classes.
+parameters for `k` classes and `n`, the counts per each class..
 
 #### Mathematical details
 
@@ -11,21 +11,21 @@ probability of these draws being made from the distribution.  The distribution
 has hyperparameters `alpha = (alpha_1,...,alpha_k)`, and probability mass
 function (pmf):
 
-```pmf(counts) = C! / (c_1!...c_k!) * Beta(alpha + c) / Beta(alpha)```
+```pmf(counts) = N! / (n_1!...n_k!) * Beta(alpha + c) / Beta(alpha)```
 
-where above `C = sum_j c_j`, `N!` is `N` factorial, and
+where above `N = sum_j n_j`, `N!` is `N` factorial, and
 `Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the multivariate beta
 function.
 
-This is a mixture distribution in that `N` samples can be produced by:
+This is a mixture distribution in that `M` samples can be produced by:
   1. Choose class probabilities `p = (p_1,...,p_k) ~ Dir(alpha)`
-  2. Draw integers `m = (m_1,...,m_k) ~ Multinomial(p, N)`
+  2. Draw integers `m = (n_1,...,n_k) ~ Multinomial(N, p)`
 
 This class provides methods to create indexed batches of Dirichlet
 Multinomial distributions.  If the provided `alpha` is rank 2 or higher, for
 every fixed set of leading dimensions, the last dimension represents one
 single Dirichlet Multinomial distribution.  When calling distribution
-functions (e.g. `dist.pdf(counts)`), `alpha` and `counts` are broadcast to the
+functions (e.g. `dist.pmf(counts)`), `alpha` and `counts` are broadcast to the
 same shape (if possible).  In all cases, the last dimension of alpha/counts
 represents single Dirichlet Multinomial distributions.
 
@@ -33,7 +33,8 @@ represents single Dirichlet Multinomial distributions.
 
 ```python
 alpha = [1, 2, 3]
-dist = DirichletMultinomial(alpha)
+n = 2
+dist = DirichletMultinomial(n, alpha)
 ```
 
 Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
@@ -41,40 +42,50 @@ The distribution functions can be evaluated on counts.
 
 ```python
 # counts same shape as alpha.
-counts = [0, 2, 0]
-dist.pdf(counts)  # Shape []
+counts = [0, 0, 2]
+dist.pmf(counts)  # Shape []
 
 # alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts.
-counts = [[11, 22, 33], [44, 55, 66]]
-dist.pdf(counts)  # Shape [2]
+counts = [[1, 1, 0], [1, 0, 1]]
+dist.pmf(counts)  # Shape [2]
 
 # alpha will be broadcast to shape [5, 7, 3] to match counts.
 counts = [[...]]  # Shape [5, 7, 3]
-dist.pdf(counts)  # Shape [5, 7]
+dist.pmf(counts)  # Shape [5, 7]
 ```
 
 Creates a 2-batch of 3-class distributions.
 
 ```python
 alpha = [[1, 2, 3], [4, 5, 6]]  # Shape [2, 3]
-dist = DirichletMultinomial(alpha)
+n = [3, 3]
+dist = DirichletMultinomial(n, alpha)
 
-# counts will be broadcast to [[11, 22, 33], [11, 22, 33]] to match alpha.
-counts = [11, 22, 33]
-dist.pdf(counts)  # Shape [2]
+# counts will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha.
+counts = [2, 1, 0]
+dist.pmf(counts)  # Shape [2]
 ```
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.__init__(alpha)` {#DirichletMultinomial.__init__}
+#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, name='DirichletMultinomial', allow_arbitrary_counts=False)` {#DirichletMultinomial.__init__}
 
 Initialize a batch of DirichletMultinomial distributions.
 
 ##### Args:
 
 
-*  <b>`alpha`</b>: Shape `[N1,..., Nn, k]` positive `float` or `double` tensor with
-    `n >= 0`.  Defines this as a batch of `N1 x ... x Nn` different `k`
-    class Dirichlet multinomial distributions.
+*  <b>`n`</b>: Non-negative `float` or `double` tensor with shape
+    broadcastable to `[N1,..., Nm]` with `m >= 0`.  Defines this as a batch
+    of `N1 x ... x Nm` different Dirichlet multinomial distributions. Its
+    components should be equal to integral values.
+*  <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to
+    `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
+     different `k` class Dirichlet multinomial distributions.
+*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
+*  <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf
+    allows for the `counts` tensor to be non-integral values.
+    The pmf/cdf are functions that can be evaluated at non-integral values,
+    but are only a distribution over non-negative integers.
 
 
 *  <b>`Examples`</b>: 
@@ -82,10 +93,10 @@ Initialize a batch of DirichletMultinomial distributions.
 ```python
 # Define 1-batch of 2-class Dirichlet multinomial distribution,
 # also known as a beta-binomial.
-dist = DirichletMultinomial([1.1, 2.0])
+dist = DirichletMultinomial(2.0, [1.1, 2.0])
 
 # Define a 2-batch of 3-class distributions.
-dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 ```
 
 
@@ -93,7 +104,26 @@ dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha}
 
-Parameters defining this distribution.
+Parameter defining this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.batch_shape(name='batch_shape')` {#DirichletMultinomial.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`
 
 
 - - -
@@ -107,7 +137,56 @@ Parameters defining this distribution.
 
 #### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
 
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.entropy(name=None)` {#DirichletMultinomial.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.event_shape(name='event_shape')` {#DirichletMultinomial.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.DirichletMultinomial.get_batch_shape()` {#DirichletMultinomial.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.DirichletMultinomial.get_event_shape()` {#DirichletMultinomial.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  event shape
 
 
 - - -
@@ -119,23 +198,25 @@ Parameters defining this distribution.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name=None)` {#DirichletMultinomial.log_pmf}
+#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name='log_pmf')` {#DirichletMultinomial.log_pmf}
 
 `Log(P[counts])`, computed for every batch member.
 
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
+For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
+that after sampling `n` draws from this Dirichlet Multinomial
+distribution, the number of draws falling in class `j` is `n_j`.  Note that
 different sequences of draws can result in the same counts, thus the
 probability includes a combinatorial coefficient.
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+*  <b>`counts`</b>: Non-negative `float` or `double` tensor whose shape can
     be broadcast with `self.alpha`.  For fixed leading dimensions, the last
     dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
+    distribution in `self.alpha`. `counts` is only legal if it sums up to
+    `n` and its components are equal to integral values. The second
+    condition is relaxed if `allow_arbitrary_counts` is set.
 *  <b>`name`</b>: Name to give this Op, defaults to "log_pmf".
 
 ##### Returns:
@@ -152,14 +233,21 @@ Class means for every batch member.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.num_classes` {#DirichletMultinomial.num_classes}
+#### `tf.contrib.distributions.DirichletMultinomial.n` {#DirichletMultinomial.n}
+
+Parameter defining this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.name` {#DirichletMultinomial.name}
 
-Tensor providing number of classes in each batch member.
+Name to prepend to all ops.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name=None)` {#DirichletMultinomial.pmf}
+#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name='pmf')` {#DirichletMultinomial.pmf}
 
 `P[counts]`, computed for every batch member.
 
@@ -172,10 +260,12 @@ probability includes a combinatorial coefficient.
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+*  <b>`counts`</b>: Non-negative `float`, `double` tensor whose shape can
     be broadcast with `self.alpha`.  For fixed leading dimensions, the last
     dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
+    distribution in `self.alpha`. `counts` is only legal if it sums up to
+    `n` and its components are equal to integral values. The second
+    condition is relaxed if `allow_arbitrary_counts` is set.
 *  <b>`name`</b>: Name to give this Op, defaults to "pmf".
 
 ##### Returns:
@@ -183,3 +273,23 @@ probability includes a combinatorial coefficient.
   Probabilities for each record, shape `[N1,...,Nn]`.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.sample(n, seed=None, name=None)` {#DirichletMultinomial.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+