import org.gradle.internal.classpath.ClassPath;

import java.util.List;
import java.util.Map;

public interface DependenciesAccessors {
    void generateAccessors(List<VersionCatalogBuilder> builders, ClassLoaderScope classLoaderScope, Settings settings);
    void createExtensions(ProjectInternal project);
    Map<String, ExternalModuleDependencyFactory> createPluginsBlockFactories(ObjectFactory objects);

import org.gradle.internal.service.scopes.Scope;

@NonNullApi
@EventScope(Scope.Build.class)
public interface ToolingModelProjectDependencyListener {

    /**
     * Notified when model builders for a {@code consumer} project requests an intermediate model of some other {@code target} project.
     * <p>
     * The {@code consumer} and {@code target} might represent the same project, and the listener implementation

            appendLine("type: $type")
        }
        testServices.assertions.assertEqualsToTestDataFileSibling(actual)
    }

    override fun configureTest(builder: TestConfigurationBuilder) {
        super.configureTest(builder)
        builder.useDirectives(Directives)
    }

    object Directives : SimpleDirectivesContainer() {
        val APPROXIMATE_TYPE by stringDirective("approximate expression type")
    }

#include "mlir/IR/Builders.h"  // from @llvm-project

namespace mlir::quant {

// Caclulate padding values for XLA ops.
// Padding values for Uniform Quantized ops can be generated with this method as
// well as it shares the same definition for padding attribute with the XLA ops.
Value CalculatePaddingAndPadIfNeeded(OpBuilder &builder, Location loc,

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

limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/mlir/tfrt/ir/mlrt/tf_mlrt_tpu_ops.h"

#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/DialectImplementation.h"  // from @llvm-project
#include "mlir/IR/OpDefinition.h"  // from @llvm-project

  auto reducer =
      builder->create<Op>(loc, block->getArgument(0), block->getArgument(1));
  builder->create<ReturnOp>(loc, reducer.getResult());
}

ConstantOp GetScalarConstOfType(Type ty, Location loc, int64_t raw_value,
                                OpBuilder* builder);

ConstantOp GetScalarNegZeroOfType(Type ty, Location loc, OpBuilder* builder);

==============================================================================*/

#include <string>

#include "llvm/ADT/ArrayRef.h"
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/Dialect.h"  // from @llvm-project
#include "mlir/IR/Types.h"  // from @llvm-project

#include "tensorflow/compiler/mlir/tensorflow/utils/convert_type.h"

#include <string>
#include <vector>

#include "llvm/Support/raw_ostream.h"
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "xla/test.h"
#include "tensorflow/core/lib/core/errors.h"

#include "llvm/Support/MathExtras.h"
#include "mlir/Bytecode/BytecodeOpInterface.h"  // from @llvm-project
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/Dialect.h"  // from @llvm-project
#include "mlir/IR/OpDefinition.h"  // from @llvm-project