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;

	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() {

                                DataType::DT_INT32));

  // Partially known shapes.
  EXPECT_EQ("tensor<?x27x?xbf16>",
            ConvertToMlirString({-1, 27, -1}, /*unknown_rank=*/false,
                                DataType::DT_BFLOAT16));

  // Unranked shapes.
  EXPECT_EQ("tensor<*xf32>",
            ConvertToMlirString({}, /*unknown_rank=*/true, DataType::DT_FLOAT));
}

}  // namespace

}


class SchemaTypeRefContext(val schema: AnalysisSchema) : TypeRefContext {
    override fun resolveRef(dataTypeRef: DataTypeRef): DataType = when (dataTypeRef) {
        is DataTypeRef.Name -> schema.dataClassesByFqName.getValue(dataTypeRef.fqName)
        is DataTypeRef.Type -> dataTypeRef.dataType
    }
}


/**
 * Represents a unique operation within a particular generation.  The invocation id should be unique within a single

  static Status CreateUninitialized(
      ImmediateExecutionContext* ctx, DataType dtype, TensorShape shape,
      absl::optional<std::string> name, const char* raw_device_name,
      const std::vector<std::string>& component_devices,
      std::unique_ptr<Variable>* output);

  // The dtype of the underlying variable.
  DataType dtype();

  // The shape of the underlying variable.
  TensorShape shape();