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+OneHotCategorical distribution.
+
+The categorical distribution is parameterized by the log-probabilities
+of a set of classes. The difference between OneHotCategorical and Categorical
+distributions is that OneHotCategorical is a discrete distribution over
+one-hot bit vectors whereas Categorical is a discrete distribution over
+positive integers. OneHotCategorical is equivalent to Categorical except
+Categorical has event_dim=() while OneHotCategorical has event_dim=K, where
+K is the number of classes.
+
+This class provides methods to create indexed batches of OneHotCategorical
+distributions.  If the provided `logits` or `probs` is rank 2 or higher, for
+every fixed set of leading dimensions, the last dimension represents one
+single OneHotCategorical distribution.  When calling distribution
+functions (e.g. `dist.prob(x)`), `logits` and `x` are broadcast to the
+same shape (if possible).  In all cases, the last dimension of `logits,x`
+represents single OneHotCategorical distributions.
+
+#### Examples
+
+Creates a 3-class distiribution, with the 2nd class, the most likely to be
+drawn from.
+
+```python
+p = [0.1, 0.5, 0.4]
+dist = OneHotCategorical(probs=p)
+```
+
+Creates a 3-class distiribution, with the 2nd class the most likely to be
+drawn from, using logits.
+
+```python
+logits = [-2, 2, 0]
+dist = OneHotCategorical(logits=logits)
+```
+
+Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
+
+```python
+# counts is a scalar.
+p = [0.1, 0.4, 0.5]
+dist = OneHotCategorical(probs=p)
+dist.prob([0,1,0])  # Shape []
+
+# p will be broadcast to [[0.1, 0.4, 0.5], [0.1, 0.4, 0.5]] to match.
+samples = [[0,1,0], [1,0,0]]
+dist.prob(samples)  # Shape [2]
+```
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.__init__(logits=None, probs=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='OneHotCategorical')` {#OneHotCategorical.__init__}
+
+Initialize OneHotCategorical distributions using class log-probabilities.
+
+##### Args:
+
+
+*  <b>`logits`</b>: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
+    set of Categorical distributions. The first `N - 1` dimensions index
+    into a batch of independent distributions and the last dimension
+    represents a vector of logits for each class. Only one of `logits` or
+    `probs` should be passed in.
+*  <b>`probs`</b>: An N-D `Tensor`, `N >= 1`, representing the probabilities of a set
+    of Categorical distributions. The first `N - 1` dimensions index into a
+    batch of independent distributions and the last dimension represents a
+    vector of probabilities for each class. Only one of `logits` or `probs`
+    should be passed in.
+*  <b>`dtype`</b>: The type of the event samples (default: int32).
+*  <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
+    parameters are checked for validity despite possibly degrading runtime
+    performance. When `False` invalid inputs may silently render incorrect
+    outputs.
+*  <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
+    (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
+    result is undefined.  When `False`, an exception is raised if one or
+    more of the statistic's batch members are undefined.
+*  <b>`name`</b>: `String` name prefixed to Ops created by this class.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.allow_nan_stats` {#OneHotCategorical.allow_nan_stats}
+
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.batch_shape` {#OneHotCategorical.batch_shape}
+
+Shape of a single sample from a single event index as a `TensorShape`.
+
+May be partially defined or unknown.
+
+The batch dimensions are indexes into independent, non-identical
+parameterizations of this distribution.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.batch_shape_tensor(name='batch_shape_tensor')` {#OneHotCategorical.batch_shape_tensor}
+
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The batch dimensions are indexes into independent, non-identical
+parameterizations of this distribution.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.cdf(value, name='cdf')` {#OneHotCategorical.cdf}
+
+Cumulative distribution function.
+
+Given random variable `X`, the cumulative distribution function `cdf` is:
+
+```
+cdf(x) := P[X <= x]
+```
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.copy(**override_parameters_kwargs)` {#OneHotCategorical.copy}
+
+Creates a deep copy of the distribution.
+
+Note: the copy distribution may continue to depend on the original
+intialization arguments.
+
+##### Args:
+
+
+*  <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization
+    arguments to override with new values.
+
+##### Returns:
+
+
+*  <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union
+    of self.parameters and override_parameters_kwargs, i.e.,
+    `dict(self.parameters, **override_parameters_kwargs)`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.covariance(name='covariance')` {#OneHotCategorical.covariance}
+
+Covariance.
+
+Covariance is (possibly) defined only for non-scalar-event distributions.
+
+For example, for a length-`k`, vector-valued distribution, it is calculated
+as,
+
+```none
+Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]
+```
+
+where `Cov` is a (batch of) `k x k` matrix, `0 <= (i, j) < k`, and `E`
+denotes expectation.
+
+Alternatively, for non-vector, multivariate distributions (e.g.,
+matrix-valued, Wishart), `Covariance` shall return a (batch of) matrices
+under some vectorization of the events, i.e.,
+
+```none
+Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]
+````
+
+where `Cov` is a (batch of) `k' x k'` matrices,
+`0 <= (i, j) < k' = reduce_prod(event_shape)`, and `Vec` is some function
+mapping indices of this distribution's event dimensions to indices of a
+length-`k'` vector.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`covariance`</b>: Floating-point `Tensor` with shape `[B1, ..., Bn, k', k']`
+    where the first `n` dimensions are batch coordinates and
+    `k' = reduce_prod(self.event_shape)`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.dtype` {#OneHotCategorical.dtype}
+
+The `DType` of `Tensor`s handled by this `Distribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.entropy(name='entropy')` {#OneHotCategorical.entropy}
+
+Shannon entropy in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.event_shape` {#OneHotCategorical.event_shape}
+
+Shape of a single sample from a single batch as a `TensorShape`.
+
+May be partially defined or unknown.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.event_shape_tensor(name='event_shape_tensor')` {#OneHotCategorical.event_shape_tensor}
+
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.event_size` {#OneHotCategorical.event_size}
+
+Scalar `int32` tensor: the number of classes.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.is_continuous` {#OneHotCategorical.is_continuous}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.is_scalar_batch(name='is_scalar_batch')` {#OneHotCategorical.is_scalar_batch}
+
+Indicates that `batch_shape == []`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.is_scalar_event(name='is_scalar_event')` {#OneHotCategorical.is_scalar_event}
+
+Indicates that `event_shape == []`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.log_cdf(value, name='log_cdf')` {#OneHotCategorical.log_cdf}
+
+Log cumulative distribution function.
+
+Given random variable `X`, the cumulative distribution function `cdf` is:
+
+```
+log_cdf(x) := Log[ P[X <= x] ]
+```
+
+Often, a numerical approximation can be used for `log_cdf(x)` that yields
+a more accurate answer than simply taking the logarithm of the `cdf` when
+`x << -1`.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.log_prob(value, name='log_prob')` {#OneHotCategorical.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.log_survival_function(value, name='log_survival_function')` {#OneHotCategorical.log_survival_function}
+
+Log survival function.
+
+Given random variable `X`, the survival function is defined:
+
+```
+log_survival_function(x) = Log[ P[X > x] ]
+                         = Log[ 1 - P[X <= x] ]
+                         = Log[ 1 - cdf(x) ]
+```
+
+Typically, different numerical approximations can be used for the log
+survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+  `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type
+    `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.logits` {#OneHotCategorical.logits}
+
+Vector of coordinatewise logits.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.mean(name='mean')` {#OneHotCategorical.mean}
+
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.mode(name='mode')` {#OneHotCategorical.mode}
+
+Mode.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.name` {#OneHotCategorical.name}
+
+Name prepended to all ops created by this `Distribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#OneHotCategorical.param_shapes}
+
+Shapes of parameters given the desired shape of a call to `sample()`.
+
+This is a class method that describes what key/value arguments are required
+to instantiate the given `Distribution` so that a particular shape is
+returned for that instance's call to `sample()`.
+
+Subclasses should override class method `_param_shapes`.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to
+    `sample()`.
+*  <b>`name`</b>: name to prepend ops with.
+
+##### Returns:
+
+  `dict` of parameter name to `Tensor` shapes.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.param_static_shapes(cls, sample_shape)` {#OneHotCategorical.param_static_shapes}
+
+param_shapes with static (i.e. `TensorShape`) shapes.
+
+This is a class method that describes what key/value arguments are required
+to instantiate the given `Distribution` so that a particular shape is
+returned for that instance's call to `sample()`.  Assumes that
+the sample's shape is known statically.
+
+Subclasses should override class method `_param_shapes` to return
+constant-valued tensors when constant values are fed.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call
+    to `sample()`.
+
+##### Returns:
+
+  `dict` of parameter name to `TensorShape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.parameters` {#OneHotCategorical.parameters}
+
+Dictionary of parameters used to instantiate this `Distribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.prob(value, name='prob')` {#OneHotCategorical.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.probs` {#OneHotCategorical.probs}
+
+Vector of coordinatewise probabilities.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.reparameterization_type` {#OneHotCategorical.reparameterization_type}
+
+Describes how samples from the distribution are reparameterized.
+
+Currently this is one of the static instances
+`distributions.FULLY_REPARAMETERIZED`
+or `distributions.NOT_REPARAMETERIZED`.
+
+##### Returns:
+
+  An instance of `ReparameterizationType`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.sample(sample_shape=(), seed=None, name='sample')` {#OneHotCategorical.sample}
+
+Generate samples of the specified shape.
+
+Note that a call to `sample()` without arguments will generate a single
+sample.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. 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` with prepended dimensions `sample_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.stddev(name='stddev')` {#OneHotCategorical.stddev}
+
+Standard deviation.
+
+Standard deviation is defined as,
+
+```none
+stddev = E[(X - E[X])**2]**0.5
+```
+
+where `X` is the random variable associated with this distribution, `E`
+denotes expectation, and `stddev.shape = batch_shape + event_shape`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`stddev`</b>: Floating-point `Tensor` with shape identical to
+    `batch_shape + event_shape`, i.e., the same shape as `self.mean()`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.survival_function(value, name='survival_function')` {#OneHotCategorical.survival_function}
+
+Survival function.
+
+Given random variable `X`, the survival function is defined:
+
+```
+survival_function(x) = P[X > x]
+                     = 1 - P[X <= x]
+                     = 1 - cdf(x).
+```
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+  `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type
+    `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.validate_args` {#OneHotCategorical.validate_args}
+
+Python boolean indicated possibly expensive checks are enabled.
+
+
+- - -
+
+#### `tf.contrib.distributions.OneHotCategorical.variance(name='variance')` {#OneHotCategorical.variance}
+
+Variance.
+
+Variance is defined as,
+
+```none
+Var = E[(X - E[X])**2]
+```
+
+where `X` is the random variable associated with this distribution, `E`
+denotes expectation, and `Var.shape = batch_shape + event_shape`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`variance`</b>: Floating-point `Tensor` with shape identical to
+    `batch_shape + event_shape`, i.e., the same shape as `self.mean()`.
+
+