"TFL::IsTransposeTrivial($0.getType().cast<ShapedType>().getShape(), $1)">>;

// Constraint that checks if the reshape op is equivalent to a transpose op.
// This is true if the reshape op is a trivial reshape op, meaning no change in
// the order of non-identity dimensions.
def IsReshapeEquivalentToTranspose : Constraint<CPred<
  "TFL::IsReshapeEquivalentToTranspose("
    "$0.getType().cast<ShapedType>(),"
    "$1.getType().cast<ShapedType>())">>;

LogicalResult VerifyTypesCompatibility(Operation::operand_type_range types,
                                       bool mask_one_dim, Operation *op) {
  int64_t common_rank = ShapedType::kDynamic;
  llvm::SmallVector<int64_t, 4> common_dims;
  int64_t dim_to_mask = ShapedType::kDynamic;

  // Initialize common_rank with rank of the first ranked type and verify that
  // following ranked types have the same rank.

  CPred<"quant::ReshapableTo1DTensor($0.getType().cast<ShapedType>())">,
  "Checks if the value dims are all ones except the right most dim">;

def ReshapeTo1DTensor : NativeCodeCall<
  "quant::ReshapeTo1DTensor($_builder, $_loc, $0)">;

def HasEqualShape : Constraint<CPred<
  "$0.getType().cast<ShapedType>().hasRank() && "
  "$1.getType().cast<ShapedType>().hasRank() && "

OpFoldResult IdentityArithmeticOpFolder(OpT arithmetic_op,
                                        ArrayRef<Attribute> operands) {
  auto lhs_type = mlir::cast<ShapedType>(arithmetic_op.getX().getType());
  auto rhs_type = mlir::cast<ShapedType>(arithmetic_op.getY().getType());
  auto result_type =
      mlir::cast<ShapedType>(arithmetic_op.getResult().getType());

  // We can fold arithmetic operation only of we can prove that we will not

using ::mlir::stablehlo::DotGeneralOp;

bool HasStaticShape(Value value) {
  auto shaped_type = mlir::dyn_cast<ShapedType>(value.getType());
  if (!shaped_type) return false;

  return shaped_type.hasStaticShape();
}

bool HasStaticShapeAtDims(Value value, const ArrayRef<int> dims) {
  auto shaped_type = mlir::dyn_cast<ShapedType>(value.getType());
  if (!shaped_type || !shaped_type.hasRank()) return false;

  for (auto dim : dims) {

  if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr)) {
    return bool_attr.getValue();
  }
  return std::nullopt;
}

ShapedType GetNhwcReturnTypeFromNchw(Operation* old_op) {
  auto composite_result_shape =
      mlir::cast<ShapedType>(old_op->getResults().front().getType()).getShape();
  std::array<int64_t, 4> output_shape;
  // NHWC <- NCHW
  output_shape[0] = composite_result_shape[0];

    Operation *op = srcop.getOperation();
    bool allTypesFp = true;
    bool allTypesQuantizedOrInt = true;
    for (auto operand : op->getOperands()) {
      ShapedType type = mlir::dyn_cast<ShapedType>(operand.getType());
      if (!type) continue;
      allTypesFp &= !mlir::isa<quant::QuantizedType>(type.getElementType());
      allTypesQuantizedOrInt &=

  }
}

// Returns a Constant op with a splat vector value.
absl::StatusOr<arith::ConstantOp> CreateConstOpWithVectorValue(
    PatternRewriter* rewriter, Location loc, ShapedType shaped_type,
    int value) {
  ShapedType dense_type = RankedTensorType::get(shaped_type.getShape(),
                                                shaped_type.getElementType());
  auto attr = CreateTypedAttr(dense_type, value);

absl::StatusOr<arith::ConstantOp> CreateConstOpWithSingleValue(
    PatternRewriter* rewriter, Location loc, ShapedType shaped_type, int value);

// Returns a Constant op with a splat vector value.
absl::StatusOr<arith::ConstantOp> CreateConstOpWithVectorValue(
    PatternRewriter* rewriter, Location loc, ShapedType shaped_type, int value);

}  // namespace TFL
}  // namespace mlir

//   bool hasRank()
//   ArrayRef<int64_t> getShape()
// This includes mlir::TF::ShapeAttr and mlir::ShapedType.
template <typename ShapeContainerT>
void SetTensorShapeProto(ShapeContainerT shape, TensorShapeProto* proto) {
  if (shape.hasRank()) {
    for (int64_t dim : shape.getShape()) {
      proto->add_dim()->set_size(mlir::ShapedType::isDynamic(dim) ? -1 : dim);
    }
  } else {
    proto->set_unknown_rank(true);
  }
}