Fix chain rule to not imply second differentiation (tensorflow#3650)

Fixes part of tensorflow#3629.

tensorflow/g3doc/how_tos/adding_an_op/index.md

@@ -1011,13 +1011,13 @@ function which computes gradients with respect to the ops' inputs given
 gradients with respect to the ops' outputs.
 
 Mathematically, if an op computes \\(y = f(x)\\) the registered gradient op
-converts gradients \\(\partial / \partial y\\) with respect to \\(y\\) into
-gradients \\(\partial / \partial x\\) with respect to \\(x\\) via the chain
-rule:
+converts gradients \\(\partial L/ \partial y\\) of loss \\(L\\) with respect to
+\\(y\\) into gradients \\(\partial L/ \partial x\\) with respect to \\(x\\) via
+the chain rule:
 
-$$\frac{\partial}{\partial x}
-    = \frac{\partial}{\partial y} \frac{\partial y}{\partial x}
-    = \frac{\partial}{\partial y} \frac{\partial f}{\partial x}.$$
+$$\frac{\partial L}{\partial x}
+    = \frac{\partial L}{\partial y} \frac{\partial y}{\partial x}
+    = \frac{\partial L}{\partial y} \frac{\partial f}{\partial x}.$$
 
 In the case of `ZeroOut`, only one entry in the input affects the output, so the
 gradient with respect to the input is a sparse "one hot" tensor.  This is