ebben
ehhez
emilyen
ennek
erre
ez
ezt
ezek
ezen
ezzel
ezért
és
fel
felé
hanem
hiszen
hogy
hogyan
igen
így
illetve
ill.
ill
ilyen
ilyenkor
ison
ismét
itt
jó
jól
jobban
kell
kellett
keresztül
keressünk
ki
kívül
között
közül
legalább
lehet
lehetett
legyen
lenne
lenni
lesz
lett
maga
magát

    function_aliases: Function name -> function alias mapping.
    asset_file_defs: `AssetFileDef`s that associates the asset files and the
      name of the tensors to which the asset file names should be fed. The
      caller should make sure the asset files exist in the output saved model
      directory.

  Raises:
    ValueError iff the graph does not contain a valid signature or the file

// the value `checkpoint_path`.
// Also adds SessionInitializer op.
absl::Status AddSessionInitializerAndInlineCheckpoint(
    ModuleOp module, absl::string_view checkpoint_path);

// Creates a pass that sinks SavedModel asset filenames to constants.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>> CreateAssetSinkingPass(
    llvm::StringRef saved_model_dir);

}  // namespace tf_saved_model
}  // namespace mlir

        this.capabilities = capabilities;
    }

    @Override
    public List<? extends Capability> getCapabilities() {
        return capabilities.asSet().asList();
    }

# - documentation
area: traffic-management

# releaseNotes is a markdown listing of any user facing changes. This will appear in the
# release notes.
releaseNotes:
- |

  "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> ()
  "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> ()
  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},
      %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y},

	return Rectangle{
		Point{r.Min.X - p.X, r.Min.Y - p.Y},
		Point{r.Max.X - p.X, r.Max.Y - p.Y},
	}
}

// Inset returns the rectangle r inset by n, which may be negative. If either
// of r's dimensions is less than 2*n then an empty rectangle near the center
// of r will be returned.
func (r Rectangle) Inset(n int) Rectangle {
	if r.Dx() < 2*n {
		r.Min.X = (r.Min.X + r.Max.X) / 2
		r.Max.X = r.Min.X
	} else {

// Sets up and runs the passes for exporting `module_op`. The behavior of the
// exporting passes is controlled by `export_opts`. Returns `AssetFileDef`s that
// associate the input arguments of @main and the asset file names. Asset file
// names will be used to feed the corresponding tensors during initialization
// upon model loading.
// TODO: b/329206105 - Add unit tests after decomposing post processing passes.

        supplier.get(), "use Optional.orNull() instead of a Supplier that returns null");
  }

  @Override
  @CheckForNull
  public T orNull() {
    return null;
  }

  @Override
  public Set<T> asSet() {
    return Collections.emptySet();
  }

  @Override
  public <V> Optional<V> transform(Function<? super T, V> function) {
    checkNotNull(function);
    return Optional.absent();
  }

  @Override

namespace mlir::quant {

// Converts arguments of the @main function that are bound to
// `tf_saved_model::AssetOp`s into regular tensor args. Returns `AsestFileDef`s
// that associates the arg with the asset.
//
// In detail, this function performs the following:
// * Replaces "tf_saved_model.bound_input" attributes to
//   "tf_saved_model.index_path", if the bound input is attached to the
//   `tf_saved_model::AssetOp`.