message: Updates the documentation for DirichletMultinomial to take floats as input.
diff: @@ -122,21 +122,22 @@ class DirichletMultinomial(distribution.Distribution):
  #### Examples
  ```python
  - alpha = [1, 2, 3]
  - n = 2
  + alpha = [1., 2., 3.]
  + n = 2.
  dist = DirichletMultinomial(n, alpha)
  ```
  - Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
  + Creates a 3-class distribution, with the 3rd class is most likely to be
  + drawn.
  The distribution functions can be evaluated on counts.
  ```python
  # counts same shape as alpha.
  - counts = [0, 0, 2]
  + counts = [0., 0., 2.]
  dist.prob(counts) # Shape []
  - # alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts.
  - counts = [[1, 1, 0], [1, 0, 1]]
  + # alpha will be broadcast to [[1., 2., 3.], [1., 2., 3.]] to match counts.
  + counts = [[1., 1., 0.], [1., 0., 1.]]
  dist.prob(counts) # Shape [2]
  # alpha will be broadcast to shape [5, 7, 3] to match counts.
  @@ -147,12 +148,12 @@ class DirichletMultinomial(distribution.Distribution):
  Creates a 2-batch of 3-class distributions.
  ```python
  - alpha = [[1, 2, 3], [4, 5, 6]] # Shape [2, 3]
  - n = [3, 3]
  + alpha = [[1., 2., 3.], [4., 5., 6.]] # Shape [2, 3]
  + n = [3., 3.]
  dist = DirichletMultinomial(n, alpha)
  - # counts will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha.
  - counts = [2, 1, 0]
  + # counts will be broadcast to [[2., 1., 0.], [2., 1., 0.]] to match alpha.
  + counts = [2., 1., 0.]
  dist.prob(counts) # Shape [2]
  ```
  