result = append(result, limitedResource)
		}
	}
	return result
}

// limitedByDefault determines from the specified usage and limitedResources the set of resources names
// that must be present in a covering quota.  It returns empty set if it was unable to determine if
// a resource was not limited by default.
func limitedByDefault(usage corev1.ResourceList, limitedResources []resourcequotaapi.LimitedResource) []corev1.ResourceName {

`--dependency <dependency>` (mandatory)::
The dependency to investigate.
You can supply a complete `group:name`, or part of it.
If multiple dependencies match, Gradle generates a report covering all matching dependencies.
`--configuration <name>` (mandatory)::
The dependency configuration which resolves the given dependency.

 *       when using Executors.
 * </ol>
 *
 * <p><b>Other notes</b>
 *
 * <ul>
 *   <li>Usually, there is one testcase method per JSR166 method covering "normal" operation, and
 *       then as many exception-testing methods as there are exceptions the method can throw.
 *       Sometimes there are multiple tests per JSR166 method when the different "normal" behaviors

		}
	}
}

func (sp *sourcePrinter) expandAddresses(rpt *Report, addrs map[uint64]addrInfo, flat map[uint64]int64) {
	// We found interesting addresses (ones with non-zero samples) above.
	// Get covering address ranges and disassemble the ranges.
	ranges, unprocessed := sp.splitIntoRanges(rpt.prof, addrs, flat)
	sp.handleUnprocessed(addrs, unprocessed)

	// Trim ranges if there are too many.
	const maxRanges = 25

// RUN: odml-to-stablehlo-opt -composite-lowering -verify-diagnostics %s | FileCheck %s

func.func @hardswish(%arg0: tensor<2xf32>) -> (tensor<*xf32>) {
  %0 = mhlo.composite "aten.hardswish.default" %arg0 {decomposition = @XlaCallModule_aten.hardswish.default.impl_0} : (tensor<2xf32>) -> tensor<2xf32>
  %1 = "tf.Identity"(%0) {device = ""} : (tensor<2xf32>) -> tensor<*xf32>
  %2 = "tf.Identity"(%1) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>

  auto func = getOperation();

  // The pattern includes
  // - Converting XlaDotV2Op to EinsumOp
  // - Converting XlaGatherOp to SliceOp
  RewritePatternSet patterns(ctx);
  populateWithGenerated(patterns);

  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    func.emitError() << "quant-converting-tf-xla-op-to-tf-op failed.";
    signalPassFailure();
  }
}

}  // namespace

distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <algorithm>
#include <iterator>
#include <memory>
#include <optional>

distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <algorithm>
#include <memory>
#include <utility>
#include <vector>

distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <optional>
#include <string>
#include <utility>

distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.h"

#include <cstdint>