message: Cleanup markdown errors in `Bijector`.
diff: @@ -114,7 +114,7 @@ class _Mapping(collections.namedtuple(
  @six.add_metaclass(abc.ABCMeta)
  @tf_export("distributions.bijectors.Bijector")
  class Bijector(object):
  - """Interface for transformations of a `Distribution` sample.
  + r"""Interface for transformations of a `Distribution` sample.
  Bijectors can be used to represent any differentiable and injective
  (one to one) function defined on an open subset of `R^n`. Some non-injective
  @@ -122,27 +122,24 @@ class Bijector(object):
  #### Mathematical Details
  - A `Bijector` implements a
  - [diffeomorphism](https://en.wikipedia.org/wiki/Diffeomorphism), i.e., a
  - bijective, differentiable function. A `Bijector` is used by
  - `TransformedDistribution` but can be generally used for transforming a
  - `Distribution` generated `Tensor`. A `Bijector` is characterized by three
  - operations:
  -
  - 1. Forward Evaluation
  + A `Bijector` implements a [smooth covering map](
  + https://en.wikipedia.org/wiki/Local_diffeomorphism), i.e., a local
  + diffeomorphism such that every point in the target has a neighborhood evenly
  + covered by a map ([see also](
  + https://en.wikipedia.org/wiki/Covering_space#Covering_of_a_manifold)).
  + A `Bijector` is used by `TransformedDistribution` but can be generally used
  + for transforming a `Distribution` generated `Tensor`. A `Bijector` is
  + characterized by three operations:
  + 1. Forward\
  Useful for turning one random outcome into another random outcome from a
  different distribution.
  -
  - 2. Inverse Evaluation
  -
  + 2. Inverse\
  Useful for "reversing" a transformation to compute one probability in
  terms of another.
  -
  - 3. (log o det o Jacobian o inverse)(x)
  -
  + 3. `(log o det o Jacobian o inverse)(x)`\
  "The log of the determinant of the matrix of all first-order partial
  - derivatives of the inverse function."
  + derivatives of the inverse function."\
  Useful for inverting a transformation to compute one probability in terms
  of another. Geometrically, the det(Jacobian) is the volume of the
  transformation and is used to scale the probability.
  