namespace tensorflow {
namespace experimental {
namespace cc {

// FunctionMetadata stores additional function information, including
// optional signaturedef feeds/fetches (for TF1-based ConcreteFunctions),
// a valid function path (for TF2-based ConcreteFunctions), and
// the types + number of inputs and outputs.
class FunctionMetadata final {

// <endcallsites>
// <endfuncpreamble>
func T_chained_indirect_call(x, y int) {
	// Here 'returnsFunc' returns an inlinable func that feeds
	// directly into a call (no named intermediate).
	G += returnsFunc(x - y)(x + y)
}

// returns2.go T_chained_conc_iface_call 144 0 1
// <endpropsdump>
// {"Flags":0,"ParamFlags":[0,0],"ResultFlags":null}

  func::FuncOp func = getOperation();
  // Convert all the DeviceIndex ops to constant values.
  func.getBody().walk([](TF::DeviceIndexOp op) {
    // This just selects the default in all cases where DeviceIndex feeds into
    // tf.Case. This could be enhanced to have some sort of policy in the
    // future.
    OpBuilder b(op);
    RankedTensorType type = RankedTensorType::get({}, b.getIntegerType(32));

http://code.google.com/p/rietveld/issues/detail?id=155

The server side of the protocol is trivial:
  1. add a <link rel="hub" href="hub-server"> tag to all
     feeds that will be pubsubhubbubbed.
  2. every time one of those feeds changes, tell the hub
     with a simple POST request.

I have tested this by adding debug prints to a local hub
server and checking that the server got the right publish
requests.

  }

  // Don't prune if the condition block argument has any user.
  if (!cond_block_argument.use_empty()) return false;

  // Don't prune if `body_yield_operand` has more than one use (that would mean
  // it feeds into another op apart from `Yield`).
  if (!body_yield_operand.hasOneUse()) return false;

  // Don't prune if any other result of the candidate op is used.
  for (Value candidate_result : candidate_op->getResults()) {

    }
    i += Character.charCount(codePoint)
  }
}

/**
 * Returns a substring of `input` on the range `[pos..limit)` with the following
 * transformations:
 *
 *  * Tabs, newlines, form feeds and carriage returns are skipped.
 *
 *  * In queries, ' ' is encoded to '+' and '+' is encoded to "%2B".
 *
 *  * Characters in `encodeSet` are percent-encoded.
 *

		sentErr := false
		sendErr := func(err error) {
			if !sentErr {
				errch <- err
				sentErr = true
			}
		}

		// Copy our data to gzipWriter, which compresses it to
		// gzipWriter, which feeds it to bodyReader.
		if _, err := io.Copy(gzipWriter, dataReader); err != nil && err != io.ErrClosedPipe {
			sendErr(err)
		}
		if err := gzipWriter.Close(); err != nil && err != io.ErrClosedPipe {
			sendErr(err)

  llvm::SmallDenseSet<mlir::StringAttr, 4> ops_to_preserve_ids_;
};

// Checks if a tf_executor.Graph can be pruned.
// For TensorFlow V1.0 compatibility: when importing a graph without providing
// feeds/fetches/targets we should not attempt to prune. The best approximation
// here is to check if the graph is of the "main" function and does not have the
// "tf.entry_function" attribute defined.
bool CanPruneGraph(func::FuncOp func) {

		Status: metav1.StatusSuccess,
	}})
	metrics.IncStreamTranslatorRequest(req.Context(), strconv.Itoa(http.StatusOK))
}

// translatorSizeQueue feeds the size events from the WebSocket
// resizeChan into the SPDY client input. Implements TerminalSizeQueue
// interface.
type translatorSizeQueue struct {
	resizeChan chan remotecommand.TerminalSize
}

  // inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  // For V1 compatibility, we process a module where the graph does not have
  // feeds and fetched. We extract first the TPU computation in a submodule,
  // where it'll be in a function with args and returned values, much more
  // like a TF v2 module. We can then run the usual pipeline on this nested