{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

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

        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,

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,

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.def"

    default:
      return errors::Unimplemented(absl::StrCat(
          "Converting DataType '", DataTypeString(dtype), "' to MLIR Type"));
  }
}

Status ConvertScalarTypeToDataType(Type type, DataType* dtype) {
  if (type.isF16()) {
    *dtype = DT_HALF;
    return absl::OkStatus();
  } else if (type.isF32()) {
    *dtype = DT_FLOAT;

// of `device_mgr`.
EagerContextPtr CreateTestingEagerContext(DeviceMgr* device_mgr);

// Converts a tensorflow::DatatypeSet to std::vector<DataType>.
// This is useful for tests using GTest's ::testing::ValuesIn, since
// DataTypeSet doesn't fullfill all the constraints of an STL-like iterable.
std::vector<DataType> DataTypeSetToVector(DataTypeSet set);

// Returns a vector of shapes intended to be "interesting" test cases.

	Imports string

	// FuncSuffix is appended to all function names in this variant's code. All
	// suffixes should be unique within a package.
	FuncSuffix string

	// DataType is the type of the data parameter of functions in this variant's
	// code.
	DataType string

	// TypeParam is the optional type parameter for the function.
	TypeParam string

	// ExtraParam is an extra parameter to pass to the function. Should begin with

      /* async= */ false, device_mgr,
      /* device_mgr_owned= */ false, /* rendezvous= */ nullptr,
      /* cluster_flr= */ nullptr));
}

std::vector<DataType> DataTypeSetToVector(DataTypeSet set) {
  std::vector<DataType> result;
  result.reserve(set.size());
  for (DataType dt : set) {
    result.push_back(dt);
  }
  return result;
}

std::vector<std::vector<int64_t>> InterestingShapes() {