ref: Optional[str] = Field(default=None, alias="$ref")
    dynamicRef: Optional[str] = Field(default=None, alias="$dynamicRef")
    defs: Optional[Dict[str, "SchemaOrBool"]] = Field(default=None, alias="$defs")
    comment: Optional[str] = Field(default=None, alias="$comment")
    # Ref: JSON Schema 2020-12: https://json-schema.org/draft/2020-12/json-schema-core.html#name-a-vocabulary-for-applying-s

			}
			ident := idents[test.name][0]
			def := info.Defs[ident]
			if def == test.obj {
				t.Fatalf("info.Defs[%s] contains the test object", test.name)
			}
			if orig := originObject(test.obj); def != orig {
				t.Errorf("info.Defs[%s] does not match obj.Origin()", test.name)
			}
			if def.Pkg() != test.obj.Pkg() {
				t.Errorf("Pkg() = %v, want %v", def.Pkg(), test.obj.Pkg())
			}

#include <array>
#include <set>

#include "absl/memory/memory.h"
#include "absl/strings/numbers.h"
#include "absl/strings/str_split.h"
#include "tensorflow/compiler/jit/defs.h"
#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/compiler/jit/kernels/xla_ops.h"
#include "tensorflow/compiler/jit/xla_device.h"
#include "tensorflow/compiler/jit/xla_device_ops.h"

  // names to their corresponding nodes at load time in order to look them up
  // in constant time.
  tensorflow::StatusOr<const tensorflow::NodeDef*> GetGraphDefNode(
      std::string name);

  // Returns a list of function defs in the SavedModel.
  const protobuf::RepeatedPtrField<FunctionDef>& GetFunctionDefs();

  // Returns a BundleReader for reading variable values.
  //
  // This TFPackage retains ownership of the underlying reader.

			}
			ident := idents[test.name][0]
			def := info.Defs[ident]
			if def == test.obj {
				t.Fatalf("info.Defs[%s] contains the test object", test.name)
			}
			if orig := originObject(test.obj); def != orig {
				t.Errorf("info.Defs[%s] does not match obj.Origin()", test.name)
			}
			if def.Pkg() != test.obj.Pkg() {
				t.Errorf("Pkg() = %v, want %v", def.Pkg(), test.obj.Pkg())
			}

	file, err := parser.ParseFile(fset, "codes.go", nil, parser.ParseComments)
	if err != nil {
		t.Fatal(err)
	}
	conf := Config{Importer: importer.Default()}
	info := &Info{
		Types: make(map[ast.Expr]TypeAndValue),
		Defs:  make(map[*ast.Ident]Object),
		Uses:  make(map[*ast.Ident]Object),
	}
	_, err = conf.Check("types", fset, []*ast.File{file}, info)
	if err != nil {
		t.Fatal(err)
	}
	for _, decl := range file.Decls {

#include "tensorflow/compiler/jit/cluster_scoping_pass.h"

#include "absl/algorithm/container.h"
#include "absl/container/flat_hash_set.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/compiler/jit/defs.h"
#include "tensorflow/compiler/jit/xla_cluster_util.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/graph/algorithm.h"

namespace tensorflow {
namespace {

// flag to disable syntactic object resolution (which also saves CPU
// and memory), and instead use the type checker [go/types] if object
// resolution is desired. See the Defs, Uses, and Implicits fields of
// the [types.Info] struct for details.
type Object struct {
	Kind ObjKind
	Name string // declared name

	n := syntax.NewName(pos, name)
	tv := syntax.TypeAndValue{Type: typ}
	tv.SetIsValue()
	n.SetTypeInfo(tv)
	r.info.Defs[n] = obj
	return obj, n
}

func (r *rewriter) generateParamName(results []*syntax.Field, i int) {
	obj, n := r.sig.RenameResult(results, i)
	r.info.Defs[n] = obj
}

// declVar declares a variable with a given name type and initializer value.

}

func canonicalMethod(pass *analysis.Pass, id *ast.Ident) {
	// Expected input/output.
	expect, ok := canonicalMethods[id.Name]
	if !ok {
		return
	}

	// Actual input/output
	sign := pass.TypesInfo.Defs[id].Type().(*types.Signature)
	args := sign.Params()
	results := sign.Results()

	// Special case: WriteTo with more than one argument,
	// not trying at all to implement io.WriterTo,