// In this case the graphs are considered equivalent; the property differences are irrelevant.
  @Test
  public void equivalent_propertiesDiffer() {
    graph.putEdge(N1, N2);

    MutableGraph<Integer> g2 =
        GraphBuilder.from(graph).allowsSelfLoops(!graph.allowsSelfLoops()).build();
    g2.putEdge(N1, N2);

    assertThat(graph).isEqualTo(g2);
  }

  // In this case the graphs are considered equivalent; the property differences are irrelevant.
  @Test
  public void equivalent_propertiesDiffer() {
    graph.putEdge(N1, N2);

    MutableGraph<Integer> g2 =
        GraphBuilder.from(graph).allowsSelfLoops(!graph.allowsSelfLoops()).build();
    g2.putEdge(N1, N2);

    assertThat(graph).isEqualTo(g2);
  }

      wrapper.CreateGraphOptimizationPassOptions(graph);

  PartiallyDeclusterPass pass;
  return pass.Run(opt_options);
}

Node* FindNodeByName(const Graph& graph, const string& name) {
  for (Node* node : graph.nodes()) {
    if (node->name() == name) {
      return node;
    }
  }
  return nullptr;
}

bool GetInputsForNode(const Graph& graph, const string& node_name,

Dependency resolution is a process that consists of two phases, which are repeated until the dependency graph is complete:

* When a new dependency is added to the graph, perform conflict resolution to determine which version should be added to the graph.
* When a specific dependency, that is a module with a version, is identified as part of the graph, retrieve its metadata so that its dependencies can be added in turn.

  private ImmutableValueGraph(ValueGraph<N, V> graph) {
    super(ValueGraphBuilder.from(graph), getNodeConnections(graph), graph.edges().size());
  }

  /** Returns an immutable copy of {@code graph}. */
  public static <N, V> ImmutableValueGraph<N, V> copyOf(ValueGraph<N, V> graph) {
    return (graph instanceof ImmutableValueGraph)
        ? (ImmutableValueGraph<N, V>) graph
        : new ImmutableValueGraph<N, V>(graph);
  }

// refactoring of graph construction code in the Python API.

namespace tensorflow {

void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input);

// Changes an attr value in the node_def Protocol Buffer and sets a status upon
// completion.
void SetAttr(TF_Graph* graph, TF_Operation* op, const char* attr_name,

import org.gradle.internal.serialize.graph.BeanStateReaderLookup
import org.gradle.internal.serialize.graph.BeanStateWriterLookup
import org.gradle.internal.serialize.graph.ClassDecoder
import org.gradle.internal.serialize.graph.ClassEncoder
import org.gradle.internal.serialize.graph.DefaultReadContext
import org.gradle.internal.serialize.graph.DefaultWriteContext
import org.gradle.internal.serialize.graph.IsolateOwner

};

// Extracts inner ops of tf_executor.island ops in a tf_executor.graph, in the
// order of ops in tf_executor.graph.
LogicalResult LiftIslandOpInnerOpsFromGraph(tf_executor::GraphOp graph) {
  auto graph_position = graph.getOperation()->getIterator();
  Block* parent_block = graph.getOperation()->getBlock();
  for (Operation& op : graph.GetBody().without_terminator()) {
    auto island_op = llvm::dyn_cast<tf_executor::IslandOp>(op);

Scope Scope::NewRootScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =
      new ShapeRefiner(graph->versions(), graph->op_registry());
  return Scope(new Impl(graph, new Status, new Impl::NameMap, refiner,
                        /* disable_shape_inference */ false));
}

Scope Scope::DisabledShapeInferenceScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =

import org.gradle.internal.serialize.graph.Codec
import org.gradle.internal.serialize.graph.ReadContext
import org.gradle.internal.serialize.graph.WriteContext
import org.gradle.internal.serialize.graph.decodePreservingSharedIdentity
import org.gradle.internal.serialize.graph.encodePreservingSharedIdentityOf
import org.gradle.internal.serialize.graph.readList
import org.gradle.internal.serialize.graph.readNonNull