* unmarshalled into that type. The preprocessor is not required to perform any type conversion but should rather
     * filter out incompatible values from its result.
     *
     * @param value The configuration value to preprocess, must not be {@code null}.
     * @param type The target type of the value, may be {@code null}.
     * @return The processed configuration value or {@code null} if none.

// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op | FileCheck %s

module {
  // For UniformQuantized depthwise convolution, tensor shape should have
  // transformed from [H,W,C,M] to [H,W,1,CxM],
  func.func @depthwise_conv(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<6xf32>} : () -> tensor<6xf32>

	return err
}

func Getcwd(buf []byte) (n int, err error) {
	defer recoverErr(&err)
	cwd := jsProcess.Call("cwd").String()
	n = copy(buf, cwd)
	return
}

func Chdir(path string) (err error) {
	if err := checkPath(path); err != nil {
		return err
	}
	defer recoverErr(&err)
	jsProcess.Call("chdir", path)
	return
}

func Fchdir(fd int) error {
	f, err := fdToFile(fd)

            }
        }

        public final AntlrResult process(AntlrSpec spec) {
            try {
                return doProcess(spec);
            } catch (ClassNotFoundException e) {
                //this shouldn't happen if you call check availability with #available first
                throw new GradleException("Cannot process antlr sources", e);
            }
        }

        /**

	processIOReadBytesMD              = NewCounterMD(processIOReadBytes, "Total bytes read by the process from the underlying storage system, /proc/[pid]/io read_bytes")

// Default MLIR dump file prefix for TensorFlow quantization passes.
inline constexpr absl::string_view kDefaultTfQuantMlirDumpFilePrefix =
    "tf_quant";

// Preprocesses the `module_op` for quantization. The preprocess steps include
// freezing the variables in the graph into constants. `is_inliner_run`
// determines whether the `InlinerPass` should be run after unfreezing.
//

  if main_bp:
    print "Created breakpoint"
  process = target.LaunchSimple(None, None, os.getcwd())
  if process:
    print "Process launched"
    listener = debugger.GetListener()
    process.broadcaster.AddListener(listener, lldb.SBProcess.eBroadcastBitStateChanged)
    while True:
      event = lldb.SBEvent()
      if listener.WaitForEvent(TIMEOUT_SECS, event):
        if lldb.SBProcess.GetRestartedFromEvent(event):
          continue

java-sourcesets-process-resources.png...

	} else if dswp.podHasBeenSeenOnce(uniquePodName) {
		// For the Pod which has been processed at least once, even though some volumes
		// may not have been reprocessed successfully this round, we still mark it as processed to avoid
		// processing it at a very high frequency. The pod will be reprocessed when volume manager calls
		// ReprocessPod() which is triggered by SyncPod.
		dswp.markPodProcessed(uniquePodName)
	}

}

// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op -quant-prepare-quantize-drq='enable-per-channel-quantization=true' | FileCheck %s

module {
  func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>