switch (itype.getWidth()) {
      case 1:
        *dtype = DT_BOOL;
        return absl::OkStatus();
      case 4:
        *dtype = itype.isUnsigned() ? DT_UINT4 : DT_INT4;
        return absl::OkStatus();
      case 8:
        *dtype = itype.isUnsigned() ? DT_UINT8 : DT_INT8;
        return absl::OkStatus();
      case 16:

			var segs bytes.Buffer
			itype := fmt.Sprintf("%s%d", typ, iWidth)
			names.WriteString("\ttest" + itype + ",\n")
			for _, fWidth := range floatWidths {
				ftype := fmt.Sprintf("float%d", fWidth)
				seg := strings.Replace(testSegment, "$F", ftype, -1)
				seg = strings.Replace(seg, "$I", itype, -1)
				segs.WriteString(seg)
			}
			body := strings.Replace(testFunc, "$I", itype, -1)
			if typ[0] == 'u' {

    } else {
      return tensorflow::Status(absl::StatusCode::kInvalidArgument,
                                "Unsupported type");
    }
  } else if (auto itype = mlir::dyn_cast<mlir::IntegerType>(element_type)) {
    if (element_type.isSignedInteger()) {
      switch (itype.getWidth()) {
        case 8:
          return DenseElementsAttr::get<int8_t>(shaped_type,

tflite::TensorType ConvertTypeToTensorType(mlir::Type type) {
  if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF64()) {
    return tflite::TensorType_FLOAT64;
  } else if (mlir::isa<mlir::TF::StringType>(type)) {
    return tflite::TensorType_STRING;

			return iAddr < jAddr
		}
		iType := retrieveListenerType(verifiedListeners[i])
		jType := retrieveListenerType(verifiedListeners[j])
		return iType < jType
	})

	printStr := "ADDRESSES\tPORT"
	if includeConfigType {
		printStr = "NAME\t" + printStr
	}
	if filter.Verbose {
		printStr += "\tMATCH\tDESTINATION"
	} else {
		printStr += "\tTYPE"
	}
	fmt.Fprintln(w, printStr)

        type, builder.getFloatAttr(element_ty, unique_index));

  if (auto qtype = mlir::dyn_cast<QuantizedType>(element_ty)) {
    mlir::RankedTensorType new_type = tensorflow::GetTypeFromTFTensorShape(
        type.getShape(), qtype.getStorageType());
    return DenseElementsAttr::get(
        new_type, builder.getIntegerAttr(qtype.getStorageType(), unique_index));
  }
  llvm_unreachable("unhandled element type");
}

    private void ambiguousClass(ClassNode type, ClassNode iType, String name) {
        if (type.getName().equals(iType.getName())) {
            addError("reference to " + name + " is ambiguous, both class " + type.getName() + " and " + iType.getName() + " match", type);
        } else {
            type.setRedirect(iType);
        }
    }

    private boolean resolveAliasFromModule(ClassNode type) {

	stringEface any = stringInterfacePtr("")
	sliceEface  any = sliceInterfacePtr(nil)

	uint16Type *_type = efaceOf(&uint16Eface)._type
	uint32Type *_type = efaceOf(&uint32Eface)._type
	uint64Type *_type = efaceOf(&uint64Eface)._type
	stringType *_type = efaceOf(&stringEface)._type
	sliceType  *_type = efaceOf(&sliceEface)._type
)

// The conv and assert functions below do very similar things.

			except Exception as e:
				print("Can't parse ", obj, ": ", e)
				continue

			if obj['data'] == 0:
				dtype = "nil"
			else:
				dtype = iface_dtype(obj)

			if dtype is None:
				print("Not an interface: ", obj.type)
				continue

			print("{0}: {1}".format(obj.type, dtype))

# TODO: print interface's methods and dynamic type's func pointers thereof.

    }
    if (ftype.isF64()) {
      return tflite::TensorType_COMPLEX128;
    }
    return Status(absl::StatusCode::kInvalidArgument, "Unsupported type");
  } else if (auto itype = mlir::dyn_cast<mlir::IntegerType>(type)) {
    switch (itype.getWidth()) {
      case 1:
        return tflite::TensorType_BOOL;
      case 4:
        if (itype.isUnsigned()) {
          return Status(absl::StatusCode::kInvalidArgument,