counted only if the ops are named 'op_name' and has 'attr_val' if
      'attr_name' is specified.
    """
    op_count = 0
    for op_name in op_names:
      # Check the main graph
      op_count += self._count_op_with_name_and_attribute(
          nodes=graphdef.node,
          op_name=op_name,
          attr_name=attr_name,
          attr_val=attr_val,
          get_op_name=get_op_name,

  // CHECK: %[[RESULT1:.*]] = "tf.Abs"(%[[RESULT0]])
  // CHECK: %[[RESULT2:.*]] = "tf.Log"(%[[RESULT1]])
  // CHECK: return %[[RESULT2]]

  %0 = "tf._UnaryOpsComposition"(%arg0) {op_names = ["Asin", "Abs", "Log"]} : (tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}


// CHECK-LABEL: @round_int
func.func @round_int(%arg0: tensor<2xi32>) -> tensor<2xi32> {

  const char* node_name = "SomeNodeName";
  const char* op_name = "FooOp";
  const char* device_name = "FakeDeviceName1";

  REGISTER_OP(op_name)
      .Input("input1: double")
      .Input("input2: uint8")
      .Output("output1: uint8")
      .Attr("SomeDataTypeAttr: type");

  TF_KernelBuilder* builder = TF_NewKernelBuilder(
      op_name, device_name, &MyCreateFunc, &MyComputeFunc, &MyDeleteFunc);

  {

  def _visit_tfr_builtins(self, op_name, args, node):
    arg_strs = []
    arg_tys = []
    for arg in args:
      value, ty = self.visit(arg)
      arg_strs.append(value)
      arg_tys.append(ty)
    tfr_op_name = 'tfr.' + op_name[5:]
    ret_tys = (
        TFR_BUILTINS[op_name](*arg_tys)
        if callable(TFR_BUILTINS[op_name]) else TFR_BUILTINS[op_name])

        op_state.operands[1] = shape_op;
      }
    }
  }
  if (op_name == "vhlo.reduce_v1" || op_name == "vhlo.reduce_window_v1" ||
      op_name == "vhlo.sort_v1" || op_name == "vhlo.scatter_v1") {
    op_state.addRegion();
  }
  if (op_name == "vhlo.while_v1") {
    op_state.addRegion();
    op_state.addRegion();
  }

  TF_OperationDescription* init(string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;
    return TF_NewOperation(

		failOnce.Do(func() {
			// (B) Evict two pods and fail this update.
			evict(podNames[1], podNames[2])
			handled = true
			err = errors.NewConflict(a.GetResource().GroupResource(), pdb.Name, fmt.Errorf("conflict"))
		})
		return handled, obj, err
	})

	// (A) Delete one pod
	if err := dc.coreClient.CoreV1().Pods("default").Delete(ctx, podNames[0], metav1.DeleteOptions{}); err != nil {
		t.Fatal(err)
	}

    for (StringRef op_name : func_count_map.keys()) {
      const int32_t quantized_count = func_count_map[op_name].num_quant;
      const int32_t total_count =
          quantized_count + func_count_map[op_name].num_float;
      lines.push_back(absl::StrFormat("%s %d/%d",
                                      pad_string(op_name, name_col_width),

	pods, err := ctrl.findPodsByPVCKey(pvcKey)
	if err != nil {
		return nil, err
	}
	podNames := []string{}
	for _, pod := range pods {
		if util.IsPodTerminated(pod, pod.Status) {
			continue
		}
		if len(pod.Spec.NodeName) == 0 {
			podNames = append(podNames, pod.Name)
		}
	}
	return podNames, nil
}

// doDeleteVolume finds appropriate delete plugin and deletes given volume, returning

}

bool IsInSubgraph(const string& func_id) { return !func_id.empty(); }

Status Encapsulator::CopySubgraphNodes(
    absl::flat_hash_map<const Node*, Node*>* node_images) {
  for (Node* node : graph_in_->op_nodes()) {
    string func_id;
    TF_RETURN_IF_ERROR(GetFunctionNameAttr(node, &func_id));
    if (!IsInSubgraph(func_id)) continue;

    Subgraph& subgraph = subgraphs_[func_id];