message: Add description of shapes and a pointer to external tutorial notebook in `tf.distributions.Distribution`.
diff: @@ -338,6 +338,27 @@ class Distribution(_BaseDistribution):
  cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
  ```
  + #### Shapes
  +
  + There are three important concepts associated with TensorFlow Distributions
  + shapes:
  + - Event shape describes the shape of a single draw from the distribution;
  + it may be dependent across dimensions. For scalar distributions, the event
  + shape is `[]`. For a 5-dimensional MultivariateNormal, the event shape is
  + `[5]`.
  + - Batch shape describes independent, not identically distributed draws, aka a
  + "collection" or "bunch" of distributions.
  + - Sample shape describes independent, identically distributed draws of batches
  + from the distribution family.
  +
  + The event shape and the batch shape are properties of a Distribution object,
  + whereas the sample shape is associated with a specific call to `sample` or
  + `log_prob`.
  +
  + For detailed usage examples of TensorFlow Distributions shapes, see
  + [this tutorial](
  + https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding%20TensorFlow%20Distributions%20Shapes.ipynb)
  +
  #### Parameter values leading to undefined statistics or distributions.
  Some distributions do not have well-defined statistics for all initialization
  