return UniformQuantizedType::getChecked(
      loc, /*flags=*/QuantizationFlags::Signed,
      /*storageType=*/IntegerType::get(&context, /*width=*/8),
      /*expressedType=*/FloatType::getF32(&context), scale, zero_point,
      /*storageTypeMin=*/llvm::minIntN(8) + (narrow_range ? 1 : 0),
      /*storageTypeMax=*/llvm::maxIntN(8));
}

UniformQuantizedType CreateI32F32UniformQuantizedType(

      {2, 2}, quant::UniformQuantizedType::get(
                  quant::QuantizationFlags::FlagValue::Signed,
                  IntegerType::get(context.get(), 8),
                  FloatType::getF32(context.get()), 3.0, 2, -128, 127));

  auto dense_attr =
      GetDenseAttrFromTensorProtoAttr(GetQint8Tensor(), result_tensor_type);

  ASSERT_TRUE(succeeded(dense_attr));

                                 mlir::FloatType::getBF16(&context)));
  ASSERT_NO_FATAL_FAILURE(VerifyConversion<float>(
      {1.0, -1.0}, DT_FLOAT, mlir::FloatType::getF32(&context)));
  ASSERT_NO_FATAL_FAILURE(VerifyConversion<double>(
      {1.0, -1.0}, DT_DOUBLE, mlir::FloatType::getF64(&context)));
  ASSERT_NO_FATAL_FAILURE(VerifyConversion<tsl::float8_e5m2>(

      return APInt(/*numBits=*/32, real_int);
    };

    auto dequant_values =
        mlir::cast<DenseIntOrFPElementsAttr>(input_values)
            .mapValues(FloatType::getF32(rewriter.getContext()),
                       llvm::function_ref<DequantizeFuncType>(dequantize_func));
    rewriter.replaceOpWithNewOp<TFL::ConstOp>(dequant_op, dequant_op.getType(),

      Type tensor_type_f32;
      if (tensor_type.hasRank()) {
        tensor_type_f32 = tensorflow::GetTypeFromTFTensorShape(
            tensor_type.getShape(), FloatType::getF32(context));
      } else {
        tensor_type_f32 = UnrankedTensorType::get(FloatType::getF32(context));
      }
      // Add cast to f32 to conform with element type of result.
      operand = rewriter.create<CastOp>(op.getLoc(), tensor_type_f32, operand);

// accumulation over the given input type.
Type GetSumAccumulationType(Type input_type) {
  MLIRContext *ctx = input_type.getContext();
  if (input_type.isBF16() || input_type.isF16()) return FloatType::getF32(ctx);
  if (input_type.isSignlessInteger(8) || input_type.isSignlessInteger(16))
    return IntegerType::get(ctx, 32);
  return input_type;
}

    auto new_result_types =
        llvm::to_vector<6>(tf_fused_batch_norm_op.getResultTypes());
    // reserve_space_3
    new_result_types.push_back(
        UnrankedTensorType::get(FloatType::getF32(rewriter.getContext())));
    auto tf_fused_batch_norm_op_v3 = CreateTfOp<TF::FusedBatchNormV3Op>(
        rewriter, tf_fused_batch_norm_op, new_result_types,
        tf_fused_batch_norm_op.getOperands(),

		//	42	off[4]
		//	46	name[namelen]
		//	46+namelen+xlen+fclen - next header
		//
		if get4(buf) != zcheader {
			break
		}
		meth := get2(buf[10:])
		size := get4(buf[24:])
		namelen := get2(buf[28:])
		xlen := get2(buf[30:])
		fclen := get2(buf[32:])
		off := get4(buf[42:])
		zname := buf[46 : 46+namelen]
		buf = buf[46+namelen+xlen+fclen:]
		if string(zname) != name {
			continue
		}

	var linkeditSeg, textSeg *macho.Segment
	loadOff := int64(machoHeaderSize64)
	get32 := mf.ByteOrder.Uint32
	for _, l := range mf.Loads {
		data := l.Raw()
		cmd, sz := get32(data), get32(data[4:])
		if cmd == LC_CODE_SIGNATURE {
			sigOff = int64(get32(data[8:]))
			sigSz = int64(get32(data[12:]))
			csCmdOff = loadOff
		}
		if seg, ok := l.(*macho.Segment); ok {
			switch seg.Name {

// See go.dev/issue/67401.
//
//go:linkname atomicwb
//go:nosplit
func atomicwb(ptr *unsafe.Pointer, new unsafe.Pointer) {
	slot := (*uintptr)(unsafe.Pointer(ptr))
	buf := getg().m.p.ptr().wbBuf.get2()
	buf[0] = *slot
	buf[1] = uintptr(new)
}

// atomicstorep performs *ptr = new atomically and invokes a write barrier.
//
//go:nosplit
func atomicstorep(ptr unsafe.Pointer, new unsafe.Pointer) {