See https://github.com/llvm/llvm-project/pull/147168 for more info.

See https://github.com/llvm/llvm-project/pull/147168 for more info.

This change standardizes the order of the parameters for `Constant[XXX]
Ops` to match with all other `Op` `build()` constructors.

In all instances of generated code for the MLIR dialects's Ops (that is
the TableGen using the .td files to create the .h.inc/.cpp.inc files),
the desired result type is always specified before the value.

Examples: 
```
// ArithOps.h.inc
class ConstantOp : public ::mlir::Op<ConstantOp, ::mlir::OpTrait::ZeroRegions, ::mlir::OpTrait::OneResult, ::mlir::OpTrait::OneTypedResult<::mlir::Type>::Impl, ::mlir::OpTrait::ZeroSuccessors, ::mlir::OpTrait::ZeroOperands, ::mlir::OpTrait::OpInvariants, ::mlir::BytecodeOpInterface::Trait, ::mlir::OpTrait::ConstantLike, ::mlir::ConditionallySpeculatable::Trait, ::mlir::OpTrait::AlwaysSpeculatableImplTrait, ::mlir::MemoryEffectOpInterface::Trait, ::mlir::OpAsmOpInterface::Trait, ::mlir::InferIntRangeInterface::Trait, ::mlir::InferTypeOpInterface::Trait> {
public:
....
static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type result, ::mlir::TypedAttr value);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypedAttr value);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::TypedAttr value);
  static void build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes = {});
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes = {});
...
```
```
// ArithOps.h.inc
class SubIOp : public ::mlir::Op<SubIOp, ::mlir::OpTrait::ZeroRegions, ::mlir::OpTrait::OneResult, ::mlir::OpTrait::OneTypedResult<::mlir::Type>::Impl, ::mlir::OpTrait::ZeroSuccessors, ::mlir::OpTrait::NOperands<2>::Impl, ::mlir::OpTrait::OpInvariants, ::mlir::BytecodeOpInterface::Trait, ::mlir::ConditionallySpeculatable::Trait, ::mlir::OpTrait::AlwaysSpeculatableImplTrait, ::mlir::MemoryEffectOpInterface::Trait, ::mlir::InferIntRangeInterface::Trait, ::mlir::arith::ArithIntegerOverflowFlagsInterface::Trait, ::mlir::OpTrait::SameOperandsAndResultType, ::mlir::VectorUnrollOpInterface::Trait, ::mlir::OpTrait::Elementwise, ::mlir::OpTrait::Scalarizable, ::mlir::OpTrait::Vectorizable, ::mlir::OpTrait::Tensorizable, ::mlir::InferTypeOpInterface::Trait> {
public:
...
static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type result, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlagsAttr overflowFlags);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlagsAttr overflowFlags);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlagsAttr overflowFlags);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type result, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlags overflowFlags = ::mlir::arith::IntegerOverflowFlags::none);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlags overflowFlags = ::mlir::arith::IntegerOverflowFlags::none);
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value lhs, ::mlir::Value rhs, ::mlir::arith::IntegerOverflowFlags overflowFlags = ::mlir::arith::IntegerOverflowFlags::none);
  static void build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes = {});
  static void build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes = {});
...
```
In comparison, in the distinct case of `ConstantIntOp` and
`ConstantFloatOp`, the ordering of the result type and the value is
switched.

Thus, this PR corrects the ordering of the aforementioned
`Constant[XXX]Ops` to match with other constructors.

This is an implementation for [RFC: Supporting Sub-Channel Quantization
in
MLIR](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694).

In order to make the review process easier, the PR has been divided into
the following commit labels:

1. **Add implementation for sub-channel type:** Includes the class
design for `UniformQuantizedSubChannelType`, printer/parser and bytecode
read/write support. The existing types (per-tensor and per-axis) are
unaltered.
2. **Add implementation for sub-channel type:** Lowering of
`quant.qcast` and `quant.dcast` operations to Linalg operations.
3. **Adding C/Python Apis:** We first define he C-APIs and build the
Python-APIs on top of those.
4. **Add pass to normalize generic ....:** This pass normalizes
sub-channel quantized types to per-tensor per-axis types, if possible.


A  design note:
- **Explicitly storing the `quantized_dimensions`, even when they can be
derived for ranked tensor.**
While it's possible to infer quantized dimensions from the static shape
of the scales (or zero-points) tensor for ranked
data tensors
([ref](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694/3)
for background), there are cases where this can lead to ambiguity and
issues with round-tripping.

```
Consider the example: tensor<2x4x!quant.uniform<i8:f32:{0:2, 0:2}, {{s00:z00, s01:z01}}>>
```

The shape of the scales tensor is [1, 2], which might suggest that only
axis 1 is quantized. While this inference is technically correct, as the
block size for axis 0 is a degenerate case (equal to the dimension
size), it can cause problems with round-tripping. Therefore, even for
ranked tensors, we are explicitly storing the quantized dimensions.
Suggestions welcome!


PS: I understand that the upcoming holidays may impact your schedule, so
please take your time with the review. There's no rush.

Full revamp of the 'quant' dialect. This is an implementation for the
RFC at
https://discourse.llvm.org/t/rfc-improvements-in-the-quant-dialect/79942