Add a short TableGen HowTo to tutorial chapter 2.

Add a note to chapter 2 of the Toy tutorial stating how to invoke
mlir-tblgen to check the generated C++ code. IMHO this is incredibly
useful when getting acquainted with TableGen/ODS.

Closes tensorflow/mlir#228

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/228 from ombre5733:toy-ch2-howto-mlir-tblgen a051a3734ca8bbf4f12027fe737aca07c64ca59d
PiperOrigin-RevId: 279518989

mlir/g3doc/Tutorials/Toy/Ch-2.md

@@ -330,6 +330,18 @@ def ConstantOp : Toy_Op<"constant", [NoSideEffect]> {
 }
 ```
 
+At this point you probably might want to know what the C++ code generated by
+TableGen looks like. Simply run the `mlir-tblgen` command with the
+`gen-op-decls` or the `gen-op-defs` action like so:
+
+```
+${build_root}/bin/mlir-tblgen -gen-op-defs ${mlir_src_root}/examples/toy/Ch2/include/toy/Ops.td -I ${mlir_src_root}/include/
+```
+
+Depending on the selected action, this will print either the `ConstantOp` class
+declaration or its implementation. Comparing this output to the hand-crafted
+implementation is incredibly useful when getting started with TableGen.
+
 #### Defining Arguments and Results
 
 With the shell of the operation defined, we can now provide the
@@ -539,7 +551,8 @@ module {
 You can build `toyc-ch2` and try yourself: `toyc-ch2 test/codegen.toy -emit=mlir
 -mlir-print-debuginfo`. We can also check our RoundTrip: `toyc-ch2
 test/codegen.toy -emit=mlir -mlir-print-debuginfo 2> codegen.mlir` followed by
-`toyc-ch2 codegen.mlir -emit=mlir`.
+`toyc-ch2 codegen.mlir -emit=mlir`. You should also use `mlir-tblgen` on the
+final definition file and study the generated C++ code.
 
 At this point MLIR knows about our Toy dialect and operations. In the
 [next chapter](Ch-3.md) we will leverage our new dialect to implement some