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)
}

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"
        }
    ],

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.

		p.bar.Write()
	}
}

func (p *Log) NewComponent(component string) *ManifestLog {
	ml := &ManifestLog{
		report: p.reportProgress(component),
	}
	p.mu.Lock()
	defer p.mu.Unlock()
	p.components[component] = ml
	return ml
}

func (p *Log) SetMessage(status string, finish bool) {
	// if we are not a terminal and there is no change, do not write

        "//tensorflow/cc/experimental/tfa:__subpackages__",
        "//tensorflow/cc/saved_model:__subpackages__",
        "//tensorflow/tools/tfg_graph_transforms:__subpackages__",
        "//waymo/onboard/ml/chauffeur_net/utils:__subpackages__",
    ],
    licenses = ["notice"],
)

cc_library(
    name = "internal_api",
    srcs = ["internal_api.cc"],
    hdrs = ["internal_api.h"],
    deps = [

	if w == nil {
		return fmt.Errorf("error: nil writer in WriteMeta")
	}
	if !finalHashComputed {
		return fmt.Errorf("error: no meta-data available (binary not built with -cover?)")
	}
	ml := rtcov.Meta.List
	return writeMetaData(w, ml, cmode, cgran, finalHash)
}

// WriteCountersDir implements [runtime/coverage.WriteCountersDir].
func WriteCountersDir(dir string) error {
	if cmode != coverage.CtrModeAtomic {

        if (pchArgs != null && !pchArgs.isEmpty()) {
            throw new UnsupportedOperationException("Precompiled header arguments cannot be specified for a Assembler compiler.");
        }
        // ml/ml64 have position sensitive arguments,
        // e.g., /Fo must appear before /c and /c must appear before the source file.

        return Iterables.concat(outputArgs, genericArgs, sourceArgs);
    }