return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));

                         const std::vector<ControlFlowInfo>& control_flow_info,
                         const TensorShapeProto& shape, Graph* graph_out) {
  DataType data_type = src_node->output_type(src_port);
  TensorProto dummy_proto;
  dummy_proto.set_dtype(data_type);
  *dummy_proto.mutable_tensor_shape() = shape;
  // Don't set any value field in the proto, since it is only going to be used
  // for shape inference.

 */
private
fun sameType(left: DataType, right: DataType) = when (left) {
    is DataClass -> right is DataClass && left.name.qualifiedName == right.name.qualifiedName
    is DataType.BooleanDataType -> right is DataType.BooleanDataType
    is DataType.IntDataType -> right is DataType.IntDataType
    is DataType.LongDataType -> right is DataType.LongDataType
    is DataType.StringDataType -> right is DataType.StringDataType

		{Name: "Name", DBName: "name", BindNames: []string{"Name"}, DataType: schema.String},
		{Name: "Age", DBName: "age", BindNames: []string{"Age"}, DataType: schema.Uint, Size: 64},
		{Name: "Birthday", DBName: "birthday", BindNames: []string{"Birthday"}, DataType: schema.Time},
		{Name: "CompanyID", DBName: "company_id", BindNames: []string{"CompanyID"}, DataType: schema.Int, Size: 64},

		{Name: "ID", DBName: "id", BindNames: []string{"ID"}, DataType: schema.Uint, PrimaryKey: true, Size: 64, Creatable: true, Updatable: true, Readable: true, HasDefaultValue: true, AutoIncrement: true},
		{Name: "Name", DBName: "", BindNames: []string{"Name"}, DataType: "", Tag: `gorm:"-"`, Creatable: false, Updatable: false, Readable: false},

namespace tensorflow {

using tsl::StatusOr;

// Converts the TensorFlow DataType 'dtype' into an MLIR (scalar) type.
Status ConvertDataType(DataType dtype, mlir::Builder builder, mlir::Type* type);

// Converts a scalar MLIR type to a TensorFlow Datatype.
Status ConvertScalarTypeToDataType(mlir::Type type, DataType* dtype);

// Converts an MLIR type to TensorFlow DataType. If 'type' is a scalar type, it

        override val type: DataType
            get() = DataTypeInternal.DefaultNullType
    }

    data class DefaultValue(
        override val type: DataType,
        override val objectOrigin: ObjectOrigin
    ) : ObjectReflection

    data class PureFunctionInvocation(
        override val type: DataType,
        override val objectOrigin: ObjectOrigin.FunctionOrigin,

import kotlinx.serialization.SerialName
import kotlinx.serialization.Serializable
import org.gradle.declarative.dsl.schema.DataType


object DataTypeInternal {

    @Serializable
    @SerialName("int")
    data object DefaultIntDataType : DataType.IntDataType {
        override val constantType: Class<Int> = Int::class.java
        override fun toString(): String = "Int"
        private

sealed interface Literal<T : Any> : Expr {
    val value: T
    val type: DataType.ConstantType<T>

    data class StringLiteral(
        override val value: String,
        override val sourceData: SourceData
    ) : Literal<String> {
        override val type: DataType.StringDataType
            get() = DataTypeInternal.DefaultStringDataType
    }

    data class IntLiteral(

            val elementType: DataType

            data class ConfiguringElementResolved(
                override val elementType: DataClass
            ) : SuccessfulElementResolution

            data class ContainerElementResolved(
                override val elementType: DataType,
                val elementFactoryFunction: SchemaMemberFunction,