}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \

  }

  private void assertEvents(String... events) {
    assertThat(stopwatch.readEventsAndClear()).isEqualTo(Arrays.toString(events));
  }

  /**
   * The stopwatch gathers events and presents them as strings. R0.6 means a delay of 0.6 seconds
   * caused by the (R)ateLimiter U1.0 means the (U)ser caused the stopwatch to sleep for a second.
   */

  }

  private void assertEvents(String... events) {
    assertThat(stopwatch.readEventsAndClear()).isEqualTo(Arrays.toString(events));
  }

  /**
   * The stopwatch gathers events and presents them as strings. R0.6 means a delay of 0.6 seconds
   * caused by the (R)ateLimiter U1.0 means the (U)ser caused the stopwatch to sleep for a second.
   */

	}
	return ast1
}

func sourceLine(n ast.Node) int {
	return fset.Position(n.Pos()).Line
}

// ParseGo populates f with information learned from the Go source code
// which was read from the named file. It gathers the C preamble
// attached to the import "C" comment, a list of references to C.xxx,
// a list of exported functions, and the actual AST, to be rewritten and
// printed.
func (f *File) ParseGo(abspath string, src []byte) {

	"cmd/internal/obj/arm64"
	"cmd/internal/obj/ppc64"
	"cmd/internal/obj/riscv"
	"cmd/internal/obj/x86"
	"cmd/internal/sys"
)

// TODO: configure the architecture

var testOut *strings.Builder // Gathers output when testing.

// append adds the Prog to the end of the program-thus-far.
// If doLabel is set, it also defines the labels collect for this Prog.
func (p *Parser) append(prog *obj.Prog, cond string, doLabel bool) {

*   TPU embedding support
    *   Added `profile_data_directory` to `EmbeddingConfigSpec` in
        `_tpu_estimator_embedding.py`. This allows embedding lookup statistics
        gathered at runtime to be used in embedding layer partitioning
        decisions.
*   PluggableDevice
    *   Third-party devices can now connect to TensorFlow as plug-ins through