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+Abstract base class for probability distributions.
+
+This class, along with `ContinuousDistribution` and `DiscreteDistribution`,
+defines the API for probability distributions.
+
+Users will never instantiate a `BaseDistribution`, but will instead
+instantiate subclasses of either `ContinuousDistribution` or
+`DiscreteDistribution`.
+
+Developers of new distributions should prefer to subclass
+`ContinuousDistribution` or `DiscreteDistribution`.
+
+### API
+
+The key methods for probability distributions are defined here. The likelihood
+functions (`pdf`, `log_pdf`) and (`pmf`, `log_pmf`) are defined in
+`ContinuousDistribution` and `DiscreteDistribution`, respectively.
+
+To keep ops generated by the distribution tied together by name, subclasses
+should override `name` and use it to preprend names of ops in other methods
+(see `cdf` for an example).
+
+Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
+and use the base class's implementation for `cdf`.
+
+### Broadcasting, batching, and shapes
+
+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`, and the likelihood
+functions defined in `ContinuousDistribution` and `DiscreteDistribution`
+reflect this broadcasting, as does the return value of `sample`.
+
+`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).
+
+Using the `Uniform` distribution as an example:
+
+```python
+minval = 3.0
+maxval = [[4.0, 6.0],
+          [10.0, 12.0]]
+
+# Broadcasting:
+# This instance represents 4 Uniform distributions. Each has a lower bound at
+# 3.0 as the `minval` parameter was broadcasted to match `maxval`'s shape.
+u = Uniform(minval, maxval)
+
+# `event_shape` is `TensorShape([])`.
+event_shape = u.get_event_shape()
+# `event_shape_t` is a `Tensor` which will evaluate to a scalar 1.
+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)
+
+# The broadcasting holds across methods. Here we use `cdf` as an example. The
+# same holds for `log_cdf` and the likelihood functions.
+
+# `cum_prob` has shape (2, 2) as the `value` argument was broadcasted to the
+# shape of the `Uniform` instance.
+cum_prob_broadcast = u.cdf(4.0)
+
+# `cum_prob`'s shape is (2, 2), one per distribution. No broadcasting
+# occurred.
+cum_prob_per_dist = u.cdf([[4.0, 5.0],
+                           [6.0, 7.0]])
+
+# INVALID as the `value` argument is not broadcastable to the distribution's
+# shape.
+cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
+```
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.batch_shape(name=None)` {#BaseDistribution.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.BaseDistribution.cdf(value, name='cdf')` {#BaseDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.dtype` {#BaseDistribution.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.entropy(name=None)` {#BaseDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.event_shape(name=None)` {#BaseDistribution.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.BaseDistribution.get_batch_shape()` {#BaseDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.get_event_shape()` {#BaseDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.log_cdf(value, name='log_cdf')` {#BaseDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.mean` {#BaseDistribution.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.name` {#BaseDistribution.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.sample(n, seed=None, name=None)` {#BaseDistribution.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`.
+
+