if err != nil {
		return err
	}
	_, _, err = ml.Secondary.Get(ctx)
	if err != nil && kerrors.IsNotFound(err) {
		return ml.Secondary.Create(ctx, ler)
	}
	return ml.Secondary.Update(ctx, ler)
}

// RecordEvent in leader election while adding meta-data
func (ml *MultiLock) RecordEvent(s string) {
	ml.Primary.RecordEvent(s)
	ml.Secondary.RecordEvent(s)
}

ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml, ml,...

			ml.data[7] = 0                    // lazy bind size
			ml.data[8] = 0                    // export
			ml.data[9] = 0                    // export size
		}

		ml := newMachoLoad(ctxt.Arch, LC_SYMTAB, 4)
		ml.data[0] = uint32(linkoff + s1 + s2)                /* symoff */
		ml.data[1] = uint32(nsortsym)                         /* nsyms */
		ml.data[2] = uint32(linkoff + s1 + s2 + s3 + s4 + s5) /* stroff */

// directory, returning an error if something went wrong.
func emitMetaDataToDirectory(outdir string, ml []rtcov.CovMetaBlob) error {
	ml, err := prepareForMetaEmit()
	if err != nil {
		return err
	}
	if len(ml) == 0 {
		return nil
	}

	metaDataEmitAttempted = true

	s := &emitState{
		metalist: ml,
		debug:    os.Getenv("GOCOVERDEBUG") != "",
		outdir:   outdir,
	}

	// Open output files.

project_path: /mlir/_project.yaml
description: An intermediate representation and compiler framework, MLIR unifies the
 infrastructure for high-performance ML models in TensorFlow.
landing_page:
  custom_css_path: /site-assets/css/style.css
  rows:
  - heading: MLIR unifies the infrastructure for high-performance ML models in TensorFlow.
    items:
    - description: >
        The <a href="https://mlir.llvm.org/" class="external">MLIR</a> project defines a common

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):

        }
    },

    // Host: x86
    // Target: x64
    LEGACY_X86_ON_AMD64("x86", "bin/amd64_x86", "lib", "ml.exe") {
        @Override
        File getCrossCompilePath(File basePath) {
            return LEGACY_AMD64_ON_AMD64.getBinPath(basePath);
        }
    },
    X86_ON_AMD64("x86", "bin/HostX64/x86", "lib/x86", "ml.exe") {
        @Override
        File getCrossCompilePath(File basePath) {

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],

					})
				}
			}
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedGetOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.GetTotal),
			})
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedHeadOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.HeadTotal),
			})
			ml = append(ml, MetricV2{

syntax = "proto3";

package third_party.tensorflow.compiler.mlir.lite.experimental.tac;

// A list of filters for TAC users to run ops/functions on ML hardwares. The
// intuition is that, for ops/functions that can be run on ML hardware (e.g.
// EdgeTPU) and TFLite CPU, TAC users give a hint that they're more performant
// to run on TFLite CPU. These filters give the TAC users freedom to specify the
// parts that they want to use other hardware to accelerate.