message: Make the explanation of tf.cond() semantics more prominent.
diff: @@ -1966,8 +1966,12 @@ def cond(pred,
  `true_fn` and `false_fn` both return lists of output tensors. `true_fn` and
  `false_fn` must have the same non-zero number and type of outputs.
  - Note that the conditional execution applies only to the operations defined in
  - `true_fn` and `false_fn`. Consider the following simple program:
  + **WARNING**: Any Tensors or Operations created outside of `true_fn` and
  + `false_fn` will be executed regardless of which branch is selected at runtime.
  +
  + Although this behavior is consistent with the dataflow model of TensorFlow,
  + it has frequently surprised users who expected a lazier semantics.
  + Consider the following simple program:
  ```python
  z = tf.multiply(a, b)
  @@ -1978,8 +1982,6 @@ def cond(pred,
  operation will not be executed. Since `z` is needed for at least one
  branch of the `cond`, the `tf.multiply` operation is always executed,
  unconditionally.
  - Although this behavior is consistent with the dataflow model of TensorFlow,
  - it has occasionally surprised some users who expected a lazier semantics.
  Note that `cond` calls `true_fn` and `false_fn` *exactly once* (inside the
  call to `cond`, and not at all during `Session.run()`). `cond`
  