tags: Collection[str],
    representative_dataset_map: rd.RepresentativeDatasetMapping,
) -> None:
  """Runs the graph for calibration in graph mode.

  This function assumes _graph mode_ (used when legacy TF1 is used or when eager
  mode is explicitly disabled) when running the graph. This step is used in
  order to collect the statistics in CustomAggregatorOp for quantization using

}

// destinationEdge is a graph edge that includes a denormalized reference to the final destination vertex.
// This should only be used when there is a single leaf vertex reachable from T.
type destinationEdge struct {
	F           graph.Node
	T           graph.Node
	Destination graph.Node
}

func newDestinationEdge(from, to, destination graph.Node) graph.Edge {

 * MutableGraph<Integer> graph = GraphBuilder.undirected().build();
 * }</pre>
 *
 * <p>{@link GraphBuilder#build()} returns an instance of {@link MutableGraph}, which is a subtype
 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),

 * MutableGraph<Integer> graph = GraphBuilder.undirected().build();
 * }</pre>
 *
 * <p>{@link GraphBuilder#build()} returns an instance of {@link MutableGraph}, which is a subtype
 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),

}

// New summarizes performance data from a profile into a graph.
func New(prof *profile.Profile, o *Options) *Graph {
	if o.CallTree {
		return newTree(prof, o)
	}
	g, _ := newGraph(prof, o)
	return g
}

// newGraph computes a graph from a profile. It returns the graph, and
// a map from the profile location indices to the corresponding graph
// nodes.

// limitations under the License.

// Package profile represents a pprof profile as a directed graph.
//
// This package is a simplified fork of github.com/google/pprof/internal/graph.
package profile

import (
	"fmt"
	"sort"
	"strings"
)

// Options encodes the options for constructing a graph
type Options struct {
	SampleValue       func(s []int64) int64 // Function to compute the value of a sample

// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

  tensorflow::DataType DataType() const override {
    return static_cast<tensorflow::DataType>(TF_OperationOutputType(output_));
  }

  tensorflow::Status Shape(

        assertThat(graph.predecessors(node)).hasSize(graph.inDegree(node));
        assertThat(graph.successors(node)).hasSize(graph.outDegree(node));
      } else {
        int selfLoopCount = graph.adjacentNodes(node).contains(node) ? 1 : 0;
        assertThat(graph.degree(node)).isEqualTo(graph.adjacentNodes(node).size() + selfLoopCount);

   */
  public static <N> Graph<N> transpose(Graph<N> graph) {
    if (!graph.isDirected()) {
      return graph; // the transpose of an undirected graph is an identical graph
    }

    if (graph instanceof TransposedGraph) {
      return ((TransposedGraph<N>) graph).graph;
    }

    return new TransposedGraph<>(graph);
  }

  /**

   */
  public static <N> Graph<N> transpose(Graph<N> graph) {
    if (!graph.isDirected()) {
      return graph; // the transpose of an undirected graph is an identical graph
    }

    if (graph instanceof TransposedGraph) {
      return ((TransposedGraph<N>) graph).graph;
    }

    return new TransposedGraph<>(graph);
  }

  /**