* If the cache already contains the outputs for the given work, an already finished {@link Deferrable} will be returned.
         * Otherwise, the execution is wrapped in a not-yet-complete {@link Deferrable} to be evaluated later.
         * The work is looked up by its {@link UnitOfWork.Identity identity} in the given cache.
         */
        <T> Deferrable<Try<T>> executeDeferred(Cache<Identity, IdentityCacheResult<T>> cache);
    }

 * limitations under the License.
 */

package org.gradle.internal.execution.steps;

import com.google.common.collect.ImmutableSortedMap;
import org.gradle.cache.Cache;
import org.gradle.internal.Deferrable;
import org.gradle.internal.Try;
import org.gradle.internal.execution.ExecutionEngine.IdentityCacheResult;
import org.gradle.internal.execution.InputFingerprinter;
import org.gradle.internal.execution.UnitOfWork;

import org.gradle.api.internal.artifacts.ivyservice.resolveengine.artifact.ResolvedArtifactSet
import org.gradle.api.internal.attributes.ImmutableAttributes
import org.gradle.internal.Deferrable
import org.gradle.internal.Try
import org.gradle.internal.component.external.model.ImmutableCapabilities
import org.gradle.internal.model.CalculatedValue
import org.gradle.internal.operations.BuildOperation

    }

    @Override
    public boolean getSupportsPluginManagementBlock() {
        return supportsPluginManagementBlock;
    }

    @Override
    public void addConfiguration(Runnable runnable, boolean deferrable) {
        throw new UnsupportedOperationException();
    }

import org.gradle.api.internal.file.FileCollectionFactory;
import org.gradle.api.internal.project.ProjectInternal;
import org.gradle.api.internal.project.ProjectStateRegistry;
import org.gradle.cache.Cache;
import org.gradle.internal.Deferrable;
import org.gradle.internal.Try;
import org.gradle.internal.buildoption.InternalOptions;
import org.gradle.internal.buildoption.StringInternalOption;
import org.gradle.internal.execution.ExecutionEngine;

    }

    @Override
    public void attachScript(Script script) {
        throw new UnsupportedOperationException();
    }

    @Override
    public void addConfiguration(Runnable runnable, boolean deferrable) {
        runnable.run();
    }

        # because:
        # - tensorflow/python/_pywrap_mlir.so already depends on LLVM anyway
        # - tensorflow/python/_pywrap_mlir.so always loaded as part of python
        #   binding
        # TODO: It might be still preferrable to place graph_optimization_pass
        # as part of the libtensorflow_framework.so, as it is the central
        # place for core related components.

  // TODO(renjieliu): Consider whether we want an allowlist for custom op as
  // well.
  virtual bool IsNotSupportedOp(mlir::Operation* op) const = 0;

  // The larger the value is, the more preferrable over CPU.
  // If the value > 1, means the hardware has advantage over CPU.
  // If the value < 1, means CPU is more preferred.
  // If we specify 10.0, meaning the hardware is 10x faster than CPU.
  // The value should be > 0.

</li>
</ul>

<p>
In all other cases, a (possibly partial) type argument list must be present.
If a type argument list is absent or partial, all missing type arguments
must be inferrable from the context in which the function is used.
</p>

<pre>
// sum returns the sum (concatenation, for strings) of its arguments.
func sum[T ~int | ~float64 | ~string](x... T) T { … }