Changed example showing gradient processing

The original example was processing the gradients twice.
1st: grads_and_vars = zip(processed_grads, var_list)
2nd: capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]

The 2nd line is especially weird because it unnecessarily zips the gradients with the var_list even though it is only processing the gradient part.

Refactored the example to be clearer, now there is only a single line that processes the gradients.

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

@@ -138,15 +138,13 @@ class OptimizerV2(trackable.Trackable):
     loss = <call_loss_function>
   vars = <list_of_variables>
   grads = tape.gradient(loss, vars)
+
+  # Do whatever you need to the gradients, for example cap them, etc.
+  # capped_grads = [MyCapper(g) for g in grads]
   processed_grads = [process_gradient(g) for g in grads]
-  grads_and_vars = zip(processed_grads, var_list)
 
-  # grads_and_vars is a list of tuples (gradient, variable).  Do whatever you
-  # need to the 'gradient' part, for example cap them, etc.
-  capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]
-
-  # Ask the optimizer to apply the capped gradients.
-  opt.apply_gradients(capped_grads_and_vars)
+  # Ask the optimizer to apply the processed gradients.
+  opt.apply_gradients(zip(processed_grads, var_list))
   ```
 
   ### Use with `tf.distribute.Strategy`.