// IntPerLayer-DAG: "tf.DumpTensor"(%[[output1_unquantized]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> ()
// IntPerLayer-DAG: return %[[output0]], %[[output1_quantized]]

  while (!nodes_to_visit.empty()) {
    const string node_name = nodes_to_visit.front();
    nodes_to_visit.pop();
    if (reachable_node_names->find(node_name) != reachable_node_names->end()) {
      continue;
    }
    reachable_node_names->insert(node_name);
    NodeDef* node = name_to_node_map.at(node_name);
    if (kVariableTypes->find(node->op()) != kVariableTypes->end()) {

    if log-dump-ssh "${node_name}" "command -v journalctl" &> /dev/null; then
        if [[ "${on_master}" == "true" ]]; then
          log-dump-ssh "${node_name}" "sudo journalctl --output=short-precise -u kube-master-installation.service" > "${dir}/kube-master-installation.log" || true

// WholeModel-DAG: %[[matmul1_q:.*]] = "tf.XlaCallModule"(%[[matmul0_q]], %[[w0]], %[[b0]]) <{Sout = [#tf_type.shape<?x2>], module = "", version = 9 : i64}> {_entry_function = @composite_dot_genera...

    const FlatSymbolRefAttr f_attr = GetFuncAttr(op);

    // In TF::PartitionedCallOp case, func_name and node_name are filled.
    // But in TF::XlaCallModuleOp case, node_name is `kEmptyNodeName` since
    // debugging and selective quantization of StableHLO Quantizer only uses
    // func_name for op matching.
    auto [func_name, node_name] = GetFuncNameAndNodeName(op, f_attr);
    std::string folder_name =

  // Check for a few expected nodes
  const char* node_name = "test_loop/cond/scalar";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/add";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/inner_loop/body/one";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/inner_loop/cond/less_than";

  return absl::OkStatus();
}

Status MakeXlaCompiledKernel(Graph* graph, const string& callee_name,
                             const string& node_name, int num_constant_args,
                             int num_resource_args, Node** result) {
  NodeDef call_node;
  call_node.set_name(node_name);
  call_node.set_op(callee_name);
  AddNodeAttr(kXlaCompiledKernelAttr, true, &call_node);

                        const absl::string_view contains) {
  for (const std::string& node_name : control_ret_node_names) {
    if (absl::StrContains(node_name, contains)) {
      VLOG(1) << "Node found: " << node_name << ", contains: " << contains;
      return node_name;
    }
  }
  VLOG(1) << "Could not find node whose name conatins: " << contains;
  return "";
}

  for (auto n : placeholder_nodes) {
    string node_name;
    int node_src_output;
    TF_RETURN_IF_ERROR(GetNodeAttr(
        n->attrs(), kOutsideCompilationOriginalNodeAttrName, &node_name));
    TF_RETURN_IF_ERROR(GetNodeAttr(
        n->attrs(), kOutsideCompilationSrcOutputAttrName, &node_src_output));
    auto iter = node_name_index.find(node_name);
    if (iter == node_name_index.end()) {

  llvm::SmallVector<Value> returning_values(output_count, Value());
  for (const auto& node_name : node_to_output_map.keys()) {
    auto node_output_tensors = node_to_output_map[node_name];

    NameLoc new_loc = NameLoc::get(builder.getStringAttr(node_name));
    int32_t max_tensor_index = 0;
    absl::c_for_each(node_output_tensors,