}

// 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.

  });
}

class ConvertGraphOp : public OpConversionPattern<tfg::GraphOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      tfg::GraphOp graph, OpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const final {
    Location loc = graph.getLoc();
    // To keep the import-as-graph logic taken by TFG, we create `void func()`

// the fetch values in the main GraphOp corresponding to the original fetch
// values from `src_func_op`. Returns an empty vector when `src_func_op` is
// empty. `main_func_op` must have a GraphOp.
SmallVector<Value> CopyOpsToMainFunction(func::FuncOp src_func_op,
                                         func::FuncOp main_func_op) {
  GraphOp src_graph_op = GetGraphOpFromFuncOp(src_func_op);
  if (!src_graph_op) {

    }
  }
  return absl::OkStatus();
}

}  // namespace

Status InferShapes(Graph* graph, const std::map<int, InferredShape>& arg_shapes,
                   const tensorflow::FunctionLibraryDefinition* fnlib_def,
                   GraphShapeInfo* shape_info) {
  ShapeRefiner shape_refiner(graph->versions(), graph->op_registry());
  shape_refiner.set_require_shape_inference_fns(false);

  std::unique_ptr<Graph> graph;
  TF_RETURN_IF_ERROR(ConvertMlirToGraph(module, configs, &graph, &flib_def));

  // If the entry function is exported to flib, then no graph is constructed.
  // Construct one in that case.
  if (configs.export_entry_func_to_flib) {
    graph = std::make_unique<Graph>(OpRegistry::Global());
    TF_RETURN_IF_ERROR(
        graph->mutable_flib_def()->AddLibrary(std::move(flib_def)));

package gif

import (
	"bufio"
	"bytes"
	"compress/lzw"
	"errors"
	"image"
	"image/color"
	"image/color/palette"
	"image/draw"
	"internal/byteorder"
	"io"
)

// Graphic control extension fields.
const (
	gcLabel     = 0xF9
	gcBlockSize = 0x04
)

var log2Lookup = [8]int{2, 4, 8, 16, 32, 64, 128, 256}

func log2(x int) int {
	for i, v := range log2Lookup {
		if x <= v {

[[viewing-debugging-dependencies]]
[[sec:debugging-build-scans]]
= View and Debug Dependencies

Gradle provides tooling to navigate dependency graphs and mitigate link:https://en.wikipedia.org/wiki/Dependency_hell[dependency hell].
Users can render the full graph of dependencies as well as identify the selection reason and origin for a dependency.

import org.gradle.internal.serialize.graph.readEnum
import org.gradle.internal.serialize.graph.readNonNull
import org.gradle.internal.serialize.graph.readPropertyValue
import org.gradle.internal.serialize.graph.withDebugFrame
import org.gradle.internal.serialize.graph.withIsolate
import org.gradle.internal.serialize.graph.withPropertyTrace
import org.gradle.internal.serialize.graph.writeCollection
import org.gradle.internal.serialize.graph.writeEnum

	// The data structure of each (provided or inferred) type represents a graph, where
	// each node corresponds to a type and each (directed) vertex points to a component
	// type. The substitution process described above repeatedly replaces type parameter
	// nodes in these graphs with the graphs of the types the type parameters stand for,
	// which creates a new (possibly bigger) graph for each type.

Value BacktrackAnalysis::BacktrackValue(Value value) {
  while (Operation* op = value.getDefiningOp()) {
    int res_index = mlir::cast<OpResult>(value).getResultNumber();
    if (auto graph = dyn_cast<tf_executor::GraphOp>(op)) {
      value = graph.GetFetch().getOperand(res_index);
    } else if (auto island = dyn_cast<tf_executor::IslandOp>(op)) {
      // Control output is generated by the IslandOp, not the yield in