[docs] Make consistent between MLIR tutorial doc and example code

[HsiangKai]

Summary

In MLIR tutorial example code, use Pure to remove the side-effects of
operations. Update the document to be consistent with the example code.

[mehdi_amini]

Thanks for the update!

Do you need someone to land this or do you have commit access?

[HsiangKai]

In D143814#4120672, @mehdi_amini wrote:

Thanks for the update!

Do you need someone to land this or do you have commit access?

I have commit access. I will land it. Thanks.

mlir/docs/Tutorials/Toy/Ch-3.md

 ```
 
 As expected, we now directly return the function argument, bypassing any
 transpose operation. However, one of the transposes still hasn't been
 eliminated. That is not ideal! What happened is that our pattern replaced the
 last transform with the function input and left behind the now dead transpose
 input. The Canonicalizer knows to clean up dead operations; however, MLIR
 conservatively assumes that operations may have side-effects. We can fix this by
-adding a new trait, `NoMemoryEffect`, to our `TransposeOp`:
+adding a new trait, `Pure`, to our `TransposeOp`:
 
 ```tablegen
-def TransposeOp : Toy_Op<"transpose", [NoMemoryEffect]> {...}
+def TransposeOp : Toy_Op<"transpose", [Pure]> {...}
 ```
 
 Let's retry now `toyc-ch3 test/transpose_transpose.toy -emit=mlir -opt`:
 
 ```mlir
 toy.func @transpose_transpose(%arg0: tensor<*xf64>) -> tensor<*xf64> {
   toy.return %arg0 : tensor<*xf64>
 }

mlir/docs/Tutorials/Toy/Ch-4.md

 the inputs to the function expect `tensor<*xf64>`. To resolve this difference,
 the inliner expects an explicit cast operation to be inserted. For this, we need
 to add a new operation to the Toy dialect, `ToyCastOp`(toy.cast), to represent
 casts between two different shapes.
 
 ```tablegen
 def CastOp : Toy_Op<"cast", [
     DeclareOpInterfaceMethods<CastOpInterface>,
-    NoMemoryEffect,
+    Pure,
     SameOperandsAndResultShape]
   > {
   let summary = "shape cast operation";
   let description = [{
     The "cast" operation converts a tensor from one type to an equivalent type
     without changing any data elements. The source and destination types
     must both be tensor types with the same element type. If both are ranked,
     then shape is required to match. The operation is invalid if converting