Node* output_node = le->dst();

        if (add_edges_to_output_of_downstream_nodes) {
          TF_RET_CHECK(output_node->type_string() == kXlaClusterOutput)
              << le->DebugString();
          nodes_to_remove.push_back(output_node);

          for (const Edge* oe : output_node->out_edges()) {
            TF_RET_CHECK(!oe->IsControlEdge());
            data_outputs[le->src_output()].push_back(

    MaybeMarkUncompilableNode(uncompilable_reason, *stack_trace,
                              encapsulating_function, uncompilable_nodes);
    VLOG(2) << "Rejecting " << call_def.DebugString() << ": "
            << uncompilable_reason << " : " << s;
    return false;
  }

  auto release_handle_on_return = gtl::MakeCleanup(
      [&] { TF_CHECK_OK(lib_runtime->ReleaseHandle(handle)); });

    auto optional_merged_node = cycles_graph_.ContractEdge(from, to);
    if (!optional_merged_node.has_value()) {
      VLOG(3) << "Could not contract " << cluster_from->DebugString(*graph_)
              << " -> " << cluster_to->DebugString(*graph_)
              << " because contracting the edge would create a cycle via "
              << DescribePotentialCycle(from, to) << ".";
      return false;
    }

        GetXlaClusterForNode(*dst);
    if (dst_cluster_name != cluster_name) {
      out_edges_to_clone.push_back(out_edge);
    }
  }

  CHECK(!out_edges_to_clone.empty()) << n->DebugString();

  NodeDef ndef = n->def();
  ndef.set_name(absl::StrCat(n->name(), "/declustered"));
  MergeDebugInfo(NodeDebugInfo(n->def()), &ndef);
  RemoveFromXlaCluster(&ndef);

      1);

  // Check the location information.
  std::vector<std::string> locations;
  for (const auto& error : collected_errors) {
    EXPECT_TRUE(error.has_location());
    locations.push_back(error.location().DebugString());
  }

  EXPECT_THAT(locations, Each(testing::HasSubstr("CALLSITELOC")));
  EXPECT_THAT(locations, Each(testing::HasSubstr(input_file)));
  EXPECT_THAT(locations, Contains(testing::HasSubstr("line: 2")));

      },
      fallback_state.device_manager(),
      fallback_state.process_function_library_runtime());
  RETURN_FAILURE_IF_ERROR(runner.status());

  VLOG(1) << "Start to evaluate node: " << node_def->get()->DebugString();

  std::vector<tensorflow::Tensor> inputs;

  // Adds inputs to the TF operation.
  for (const ElementsAttr& operand : operands) {
    tensorflow::Tensor tensor;

  InputArrays inputs;
  ss << "\ninputs: ";
  for (auto& it : inputs) {
    ss << "\n\t" << it.first << " -> "
       << DataTypeString(it.second.imported_dtype) << " "
       << it.second.shape.DebugString();
  }
  ss << "\noutputs:";
  for (auto& output : outputs) ss << " " << output;
  ss << "\ncontrol_outputs:";
  for (auto& output : control_outputs) ss << " " << output;

      absl::string_view device_name) {
    TF_ASSIGN_OR_RETURN(DeviceId device_id, GetIdFor(device_name));
    return std::cref(*id_to_device_type_[device_id.id()]);
  }

  string DebugString(const DeviceSet& device_set) const;

 private:
  absl::flat_hash_map<string, DeviceId> name_to_id_;

  // These fields are populated for a device in GetIdFor, *before* we give out a
  // DeviceId.

        return errors::Internal("Multiple return node for loop cond function ",
                                loop_cond_func->name(), ": ",
                                ret_node->DebugString(), " and ",
                                n->DebugString());
      } else {
        ret_node = n;
      }
    }
  }
  if (!ret_node) {
    return errors::Internal("No _Retval node for loop cond function ",

            return errors::Internal("Gradient for input ", x_idx,
                                    " expected shape ",
                                    x_shapes[x_idx].DebugString(), " but was ",
                                    dxout[x_idx].shape().DebugString());
          }
          const int64_t x_size = x_shapes[x_idx].num_elements();
          auto jacobian = (*jacobian_ts)[x_idx * y_num + y_idx].matrix<JAC_T>();