//===----------------------------------------------------------------------===//

TensorFlowSavedModelDialect::TensorFlowSavedModelDialect(MLIRContext *context)
    : Dialect(/*name=*/"tf_saved_model", context,
              TypeID::get<TensorFlowSavedModelDialect>()) {
  // The TensorFlow Dialect is needed in the verifier and other routines
  // associated to this dialect. It makes little sense anyway to use the

#include "mlir/Support/DebugStringHelper.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Support/TypeID.h"  // from @llvm-project
#include "mlir/Transforms/FoldUtils.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"

	f = newTypeEncoder(t, true)
	wg.Done()
	encoderCache.Store(t, f)
	return f
}

var (
	marshalerType     = reflect.TypeFor[Marshaler]()
	textMarshalerType = reflect.TypeFor[encoding.TextMarshaler]()
)

// newTypeEncoder constructs an encoderFunc for a type.
// The returned encoder only checks CanAddr when allowAddr is true.

// of the max int boundary.
// We do it this way so the test doesn't depend on ints being 32 bits.
var (
	bigInt  = fmt.Sprintf("0x%x", int(1<<uint(reflect.TypeFor[int]().Bits()-1)-1))
	bigUint = fmt.Sprintf("0x%x", uint(1<<uint(reflect.TypeFor[int]().Bits()-1)))
)

var execTests = []execTest{
	// Trivial cases.
	{"empty", "", "", nil, true},
	{"text", "some text", "some text", nil, true},

	if ovf := TypeFor[float32]().OverflowFloat(ovfFloat32); !ovf {
		t.Errorf("%v should overflow float32", ovfFloat32)
	}
	if ovf := TypeFor[float32]().OverflowFloat(-ovfFloat32); !ovf {
		t.Errorf("%v should overflow float32", -ovfFloat32)
	}

	maxInt32 := int64(0x7fffffff)
	if ovf := TypeFor[int32]().OverflowInt(maxInt32); ovf {
		t.Errorf("%v wrongly overflows int32", maxInt32)
	}

		if err == io.EOF {
			break
		}
		if err != nil {
			return fmt.Errorf("next: %v", err)
		}

		dest := filepath.Join(destination, header.Name)
		switch header.Typeflag {
		case tar.TypeDir:
			if _, err := os.Stat(dest); err != nil {
				if err := os.Mkdir(dest, 0o755); err != nil {
					return fmt.Errorf("mkdir: %v", err)
				}
			}
		case tar.TypeReg:

	defaultSampleTypeX int64
}

// ValueType corresponds to Profile.ValueType
type ValueType struct {
	Type string // cpu, wall, inuse_space, etc
	Unit string // seconds, nanoseconds, bytes, etc

	typeX int64
	unitX int64
}

// Sample corresponds to Profile.Sample
type Sample struct {
	Location []*Location
	Value    []int64
	// Label is a per-label-key map to values for string labels.
	//

	o.X = typ
	return o
}

// typeOrNil is like type_ but it returns nil if there was no type
// instead of reporting an error.
//
//	Type     = TypeName | TypeLit | "(" Type ")" .
//	TypeName = identifier | QualifiedIdent .
//	TypeLit  = ArrayType | StructType | PointerType | FunctionType | InterfaceType |
//		      SliceType | MapType | Channel_Type .
func (p *parser) typeOrNil() Expr {
	if trace {

  }
};

}  // namespace

TensorFlowExecutorDialect::TensorFlowExecutorDialect(MLIRContext *context)
    : Dialect(/*name=*/"tf_executor", context,
              TypeID::get<TensorFlowExecutorDialect>()) {
  addOperations<
#define GET_OP_LIST
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.cc.inc"
      >();

  addInterfaces<TensorFlowExecutorInlinerInterface,

		tt := (*structType)(unsafe.Pointer(t))
		for i := range tt.Fields {
			f := &tt.Fields[i]
			addTypeBits(bv, offset+f.Offset, f.Typ)
		}
	}
}

// TypeFor returns the [Type] that represents the type argument T.
func TypeFor[T any]() Type {
	var v T
	if t := TypeOf(v); t != nil {
		return t // optimize for T being a non-interface kind
	}
	return TypeOf((*T)(nil)).Elem() // only for an interface kind