namespace {

// Converts XlaCallModuleOps to func.call.
class XlaCallModuleToCallPass
    : public impl::XlaCallModuleToCallPassBase<XlaCallModuleToCallPass> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(XlaCallModuleToCallPass)

  explicit XlaCallModuleToCallPass() = default;

 private:
  void runOnOperation() override;
};

// Converts XlaCallModuleOps to func.call.

#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project

namespace mlir {
namespace quantfork {

/// Converts per-layer FakeQuant attributes to the corresponding type.
/// In the event that the parameters cannot be converted, returns a nullptr
/// convertible Type and issues an appropriate error.
/// Note that there are multiple variants of a per-layer FakeQuant op, so

		converter:        converter,
		selectableFields: selectableFields,
	}
	return &safeConverterWrapper{unsafe}, unsafe, nil
}

// crConverterInterface is the interface all cr converters must implement
type crConverterInterface interface {
	// Convert converts in object to the given gvk and returns the converted object.

absl::StatusOr<llvm::StringRef> GetTensorFlowOpName(llvm::StringRef);

// Converts an MLIR operation to TensorFlow NodeDef with given node name. This
// name should be unique to the graph it is being inserted into.
absl::StatusOr<std::unique_ptr<NodeDef>> GetOperationNodeDef(
    mlir::Operation* inst, llvm::StringRef name);

// Converts MLIR attributes with values to their tensorflow equivalent.

// the module.
std::unique_ptr<OperationPass<ModuleOp>> CreateStripNoinlineAttributePass();

// Converts tensor list operations into operations on buffers and sizes. Needs
// static shapes and known max element count.
std::unique_ptr<OperationPass<ModuleOp>> CreateTensorListOpsDecompositionPass();

// Converts tensor array ops into operations on local variables, which can later

 * </ul>
 *
 * <p>Using a converter:
 *
 * <ul>
 *   <li>Convert one instance in the "forward" direction using {@code converter.convert(a)}.
 *   <li>Convert multiple instances "forward" using {@code converter.convertAll(as)}.
 *   <li>Convert in the "backward" direction using {@code converter.reverse().convert(b)} or {@code
 *       converter.reverse().convertAll(bs)}.

	// the order of the scalar field. Elements in the Montgomery domain take the
	// form a×R and p256OrdMul calculates (a × b × R⁻¹) mod n. RR is R in the
	// domain, or R×R mod n, thus p256OrdMul(x, RR) gives x×R, i.e. converts x
	// into the Montgomery domain.
	RR := &p256OrdElement{0x83244c95be79eea2, 0x4699799c49bd6fa6,
		0x2845b2392b6bec59, 0x66e12d94f3d95620}

	p256OrdMul(_1, x, RR)      // _1
	p256OrdSqr(x, _1, 1)       // _10

        }
        return new PosixBuilder()
    }

    /**
     * Converts a list of command line elements (returned by {@link #getCommandLine()}) to a list of Java/Groovy/Kotlin string literals.
     * Literals include surrounding quotes and have special symbols escaped, so they are safe to use in the sources.
     * @param cmd the command line elements to be converted
     * @return a List of string literals
     */

#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given Jax model to a TF Lite FlatBuffer
// string according to the given model flags, toco flags and tags. Returns error
// status if it fails to convert the input.
Status ConvertJaxToTFLiteFlatBuffer(const std::string& input,
                                    const toco::ModelFlags& model_flags,

	if typeDesc != reflect.TypeOf(result) {
		return nil, fmt.Errorf("unable to convert to %v", typeDesc)
	}
	result = make(map[string]any, len(v.fields))
	for k, v := range v.fields {
		converted, err := convertField(v)
		if err != nil {
			return nil, fmt.Errorf("fail to convert field %q: %w", k, err)
		}
		result[k] = converted
	}
	return result, nil
}