"@llvm-project//mlir:FuncDialect",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:QuantOps",
        "@llvm-project//mlir:Support",
    ],
)

# Binary to apply quantization on the annotated files.
tf_cc_binary(
    name = "tfl_quantizer",
    srcs = [
        "tfl_quantizer.cc",
    ],
    deps = [
        ":quantize_model",

		if cfg.InstallConfig.AmbientEnabled {
			// Start ambient controller

			// node agent will spawn a goroutine and watch the K8S API for events,
			// as well as listen for messages from the CNI binary.
			log.Info("Starting ambient node agent with inpod redirect mode")
			ambientAgent, err := nodeagent.NewServer(ctx, watchServerReady, cfg.InstallConfig.CNIEventAddress,
				nodeagent.AmbientArgs{

        ":mlir_graph_optimization_pass",
        "//tensorflow/core:core_cpu",
    ],
    alwayslink = 1,
)

# This should just be a wrapper around tf_mlir_opt_main. Don't add
# direct dependencies to this binary.
tf_cc_binary(
    name = "tf-opt",
    testonly = True,
    deps = [
        ":tf_mlir_opt_main",
    ],
)

tf_cc_binary(
    name = "tf-reduce",
    testonly = True,

        * 응답 시, `set`는 `list`로 변환됩니다.
        * 생성된 스키마는 (JSON 스키마의 `uniqueItems`를 이용해) `set`의 값이 고유함을 명시합니다.
* `bytes`:
    * 표준 파이썬의 `bytes`.
    * 요청과 응답에서 `str`로 취급됩니다.
    * 생성된 스키마는 이것이 `binary` "형식"의 `str`임을 명시합니다.
* `Decimal`:
    * 표준 파이썬의 `Decimal`.
    * 요청과 응답에서 `float`와 동일하게 다뤄집니다.

	}
	// Read resync meta header
	switch binary.LittleEndian.Uint16(data[0:2]) {
	case resyncMetaFormat:
	default:
		return brs, fmt.Errorf("resyncMeta: unknown format: %d", binary.LittleEndian.Uint16(data[0:2]))
	}
	switch binary.LittleEndian.Uint16(data[2:4]) {
	case resyncMetaVersion:
	default:
		return brs, fmt.Errorf("resyncMeta: unknown version: %d", binary.LittleEndian.Uint16(data[2:4]))
	}

 *
 *  <h3>WriteContext</h3>
 *  <p>
 *      Before serialization starts, a {@link org.gradle.internal.serialize.graph.WriteContext} is created.
 *      The WriteContext keeps track (among other things) of the low level binary stream (actually, a Gradle serialization {@link org.gradle.internal.serialize.Encoder Encoder}) and the codec to be used.
 *  </p>
 *  <p>

      'traceparent',
      'tracestate',

      # Cloud trace context. Compatible with OpenCensusAgent and Stackdriver Istio
      # configurations.
      'x-cloud-trace-context',

      # Grpc binary trace context. Compatible with OpenCensusAgent nad
      # Stackdriver Istio configurations.
      'grpc-trace-bin',

      # b3 trace headers. Compatible with Zipkin, OpenCensusAgent, and

	}
	// Read header
	switch binary.LittleEndian.Uint16(data[0:2]) {
	case format:
	default:
		return fmt.Errorf("%s: unknown format: %d", ri.JobType, binary.LittleEndian.Uint16(data[0:2]))
	}
	switch binary.LittleEndian.Uint16(data[2:4]) {
	case version:
	default:
		return fmt.Errorf("%s: unknown version: %d", ri.JobType, binary.LittleEndian.Uint16(data[2:4]))
	}

	ri.mu.Lock()

        'can be specified as "x.y" or "compute_xy" to include both virtual and'
        ' binary GPU code, or as "sm_xy" to only include the binary '
        'code.\nPlease note that each additional compute capability '
        'significantly increases your build time and binary size, and that '
        'TensorFlow only supports compute capabilities >= 3.5 [Default is: '

# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with: