// that was extracted..

  // Find the input edges to form the set of operands to the new function call.
  llvm::SetVector<Value> inputs;
  for (Operation* op : ops) {
    for (Value operand : op->getOperands()) {
      Operation* defining_op = operand.getDefiningOp();
      if (!ops.contains(defining_op)) inputs.insert(operand);
    }
  }
  // Find the output edges to form the set of resutls of the new function call.

	// The containing function
	Func *Func

	// Storage for Succs, Preds and Values.
	succstorage [2]Edge
	predstorage [4]Edge
	valstorage  [9]*Value
}

// Edge represents a CFG edge.
// Example edges for b branching to either c or d.
// (c and d have other predecessors.)
//
//	b.Succs = [{c,3}, {d,1}]
//	c.Preds = [?, ?, ?, {b,0}]
//	d.Preds = [?, {b,1}, ?]
//

    if (stateless && stateless.getValue())
      *node_def->mutable_op() = "Stateless" + node_def->op();
  }

  // Add inputs to the NodeDef based on the number of operands. This is required
  // as later when edges are added to the Node using Graph::AddEdge the
  // associated NodeDef is not updated.
  for (int i = 0, e = inst->getNumOperands(); i < e; ++i) {
    node_def->add_input();
  }

  // that was extracted..

  // Find the input edges to form the set of operands to the new function call.
  llvm::SetVector<Value> inputs;
  for (Operation* op : ops) {
    for (Value operand : op->getOperands()) {
      Operation* defining_op = operand.getDefiningOp();
      if (!ops.contains(defining_op)) inputs.insert(operand);
    }
  }
  // Find the output edges to form the set of resutls of the new function call.

          context, {mlir::StringAttr::get(context, signature->signature_key)}));
}

// There are control nodes at each end of each control edge. For each of them,
// we store the source vertices of the incoming edges (if any) and the control
// node's output token. To improve testability, we use an ordered set for the
// source vertices.
struct ControlNodeDesc {
  std::set<int> incoming;
  std::optional<mlir::Value> outgoing;

  // Populate the model control dependencies metadata entry.
  if (std::any_of(
          model_control_dependencies_.begin(),
          model_control_dependencies_.end(),
          [](const tflite::ControlEdges& edges) { return !edges.empty(); })) {
    metadata.push_back(
        BuildMetadata(tflite::kModelControlDependenciesMetadataKey,
                      tflite::SerializeModelControlDependencies(

		name           string
		graph          trustGraphDescription
		expectedChains []string
		expectedErr    string
	}{
		{
			// Build the following graph from RFC 4158, figure 7 (note that in this graph edges represent
			// certificates where the parent is the issuer and the child is the subject.) For the certificate
			// C->B, use an unsupported ExtKeyUsage (in this case ExtKeyUsageCodeSigning) which invalidates

  options.arguments_are_tupled = false;
  options.untuple_result = true;
  // Hardcode run id to always be one: TF distributed strategy
  // differentiates between subsequent runs using dependency edges. This
  // is safe, as only TF dist-strat can produce distributed ops, and we
  // can rely on TF dist-strat invariants.
  options.launch_id = 1;
  // TODO(b/293186653): investigate we should turn on strict shape checking for

                             done);
      }

      // Hardcode run id to always be zero: TF distributed strategy
      // differentiates between subsequent runs using dependency edges. This
      // is safe, as only TF dist-strat can produce distributed ops, and we
      // can rely on TF dist-strat invariants.
      xla::RunId run_id(0);
      run_options.set_run_id(run_id);

    const XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,
    XlaCompilationResult* compilation_result) {
  // Construct mapping from XlaComputation's arg to input edges of execute
  // node.
  GetInputMappingForMlir(arg_shapes.size(), &compilation_result->input_mapping);

  // Compute all input shapes.
  TF_RETURN_IF_ERROR(GetXlaInputShapes(module_op, arg_shapes, use_tuple_args,