emplate")&&(e=e.content||e),ce.merge([],e.childNodes))}},function(r,i){ce.fn[r]=function(e,t){var n=ce.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=ce.filter(t,n)),1<this.length&&(j[r]||ce.uniqueSort(n),E.test(r)&&n.reverse()),this.pushStack(n)}});var D=/[^\x20\t\r\n\f]+/g;function N(e){return e}function q(e){throw e}function L(e,t,n,r){var i;try{e&&v(i=e.promise)?i.call(e).done(t).fail(n):e&&v(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void...

  /**
   * Returns the inverse view of this bimap, which maps each of this bimap's values to its
   * associated key. The two bimaps are backed by the same data; any changes to one will appear in
   * the other.
   *
   * <p><b>Note:</b>There is no guaranteed correspondence between the iteration order of a bimap and
   * that of its inverse.
   *
   * @return the inverse view of this bimap
   */

    return result;
  }
  std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

,"parseHTML","ready","rparentsprev","guaranteedUnique","children","contents","prev","sibling","cur","targets","l","closest","index","prevAll","add","addBack","parents","parentsUntil","nextAll","nextUntil","prevUntil","contentDocument","content","reverse","rnothtmlwhite","Identity","v","Thrower","ex","adoptValue","resolve","reject","noValue","method","promise","fail","then","Callbacks","object","_","flag","firing","memory","fired","locked","list","queue","firingIndex","fire","once","stopOnFalse",...

};

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:
  explicit GraphOperation(TF_Graph* g) : TracingOperation(kGraph), g_(g) {}
  void Release() override { delete this; }
  absl::Status Reset(const char* op, const char* raw_device_name) override {
    if (op_) {
      return errors::FailedPrecondition("Reset called on already built op.");
    }

func osMkdirAll(dirPath string, perm os.FileMode, _ string) error {
	// baseDir is not honored in windows platform
	return os.MkdirAll(dirPath, perm)
}

// readDirFn applies the fn() function on each entries at dirPath, doesn't recurse into
// the directory itself, if the dirPath doesn't exist this function doesn't return
// an error.
func readDirFn(dirPath string, filter func(name string, typ os.FileMode) error) error {

  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }
  return tensorflow::wrap(new_op);
}

void TFE_DeleteOp(TFE_Op* op) {
  if (op == nullptr) {
    return;
  }

  tensorflow::unwrap(op)->Release();
}

const char* TFE_OpGetName(const TFE_Op* op, TF_Status* status) {

		for y := range hosts {
			if x == y {
				continue
			}
			c.Local = hosts[x]
			c.Remote = hosts[y]
			cReverse.Local = hosts[y]
			cReverse.Remote = hosts[x]
			if c.shouldConnect() == cReverse.shouldConnect() {
				t.Errorf("shouldConnect(%q, %q) != shouldConnect(%q, %q)", hosts[x], hosts[y], hosts[y], hosts[x])
			}
			if c.shouldConnect() {
				should++
			}
		}
		if should < 10 {

  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),
                                         dtype, t.release(), handle_methods,
                                         status);
}

TFE_TensorHandle* CopyToLoggingDevice(TFE_Context* context,
                                      TFE_TensorHandle* tensor,

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {
    std::vector<TFE_TensorHandle*> result;
    result.reserve(tensors_.size());
    for (const TensorHandlePtr& tensor : tensors_) {
      result.emplace_back(tensor.get());
    }
    return result;
  }

 private:
  ParallelTensor(const ParallelDevice& device,