valid: false,
		},
		{
			name: "empty authority",
			redirect: &networking.HTTPRedirect{
				Uri:       "t",
				Authority: "",
			},
			valid: true,
		},
		{
			name: "empty uri",
			redirect: &networking.HTTPRedirect{
				Uri:       "",
				Authority: "t",
			},
			valid: true,
		},
		{
			name: "empty redirect code",
			redirect: &networking.HTTPRedirect{

    // TODO(prakalps): Refactor to keep tf.entry_function attribute encoding and
    // decoding in a centralized place.
    // Record the input and output mapping.
    if (!specs.inputs.empty() || !specs.outputs.empty() ||
        !specs.control_outputs.empty()) {
      mlir::Builder b(context);
      std::string s;
      llvm::raw_string_ostream ss(s);
      llvm::interleave(
          specs.inputs, ss,

				continue
			}
			// there can be only one catch all egress listener with empty port, and it should be the last listener.
			if egress.Port == nil {
				if !catchAllEgressListenerFound {
					if index == len(rule.Egress)-1 {
						catchAllEgressListenerFound = true
					} else {
						errs = AppendValidation(errs, fmt.Errorf("sidecar: the egress listener with empty port should be the last listener in the list"))
					}
				} else {

      GetStringsFromDictionaryAttr(res_attrs, kTfSavedModelIndexPathAttr);

  // If no defined saved model signature, then return empty list.
  // This can happen when we are converting model not from SavedModel.
  if (sig_def_inputs.empty() && sig_def_outputs.empty()) return {};

  // Fetch function inputs and outputs tensor names.
  auto dict_attr =

  void EnqueueCallers(func::FuncOp fn);

  // Returns the function at the front of the queue.
  func::FuncOp front() { return queue_.front(); }

  // Returns whether work queue is empty.
  bool EmptyQueue() const { return queue_.empty(); }

  // Returns function from the front of the work queue.
  func::FuncOp pop_front() {
    func::FuncOp ret = queue_.front();
    queue_.pop();
    queue_set_.erase(ret);

    if (!status->status.ok()) return false;
  }
  return true;
}

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
                              const tensorflow::TensorShape& shape) {
  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());

  auto func = mlir::cast<SymbolRefAttr>(op.getBranches()[index]);
  auto empty = rewriter.getStringAttr("");
  ReplaceTfOpWithNewOp<PartitionedCallOp>(
      rewriter, op, op.getResultTypes(), op.getOperands().drop_front(), func,
      /*config=*/empty, /*config_proto=*/empty, /*executor_type=*/empty);
  return success();
}

void CaseOp::getCanonicalizationPatterns(RewritePatternSet& results,

              }
            ],
            "description": "`exempt` specifies how requests are handled for an exempt priority level. This field MUST be empty if `type` is `\"Limited\"`. This field MAY be non-empty if `type` is `\"Exempt\"`. If empty and `type` is `\"Exempt\"` then the default values for `ExemptPriorityLevelConfiguration` apply."
          },
          "limited": {
            "allOf": [
              {

              }
            ],
            "description": "`exempt` specifies how requests are handled for an exempt priority level. This field MUST be empty if `type` is `\"Limited\"`. This field MAY be non-empty if `type` is `\"Exempt\"`. If empty and `type` is `\"Exempt\"` then the default values for `ExemptPriorityLevelConfiguration` apply."
          },
          "limited": {
            "allOf": [
              {

      "description": "Represents an empty directory for a pod. Empty directory volumes support ownership management and SELinux relabeling.",
      "properties": {
        "medium": {