TF_Operation* op;
    UseHelper(inputs, &op);
    return op;
  }

  // All the *Helper methods are used as a workaround for the restrictions that
  // one cannot call ASSERT_* methods in non-void-returning functions (when
  // exceptions are disabled during compilation)
  void UseHelper(const std::vector<TF_Output>& inputs, TF_Operation** op) {
    TF_OperationDescription* desc =

labels.crawler_title_edit=Allgemeine Konfiguration
labels.schedule=Zeitplan
labels.enabled=Aktiviert
labels.day_for_cleanup=Dokumente entfernen nach
labels.day=Tag(en)
labels.crawl_button_update=Aktualisieren
labels.none=Keine
labels.crawling_thread_count=Anzahl gleichzeitiger Crawler
labels.incremental_crawling=Prüfe "Last Modified"-Header
labels.search_log_enabled=Suchen protokollieren
labels.user_info_enabled=Benutzer protokollieren

# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(
    name = "oss",
    flag_values = {":oss_setting": "True"},
    visibility = ["//visibility:public"],
)

# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",

    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;
    }

    int length = toIndex - fromIndex;
    // Obtain m = (-distance mod length), a non-negative value less than "length". This is how many
    // places left to rotate.
    int m = -distance % length;
    m = (m < 0) ? m + length : m;

		panic("oops")
	}
	return 0, io.EOF
}

// Make sure that an empty Buffer remains empty when
// it is "grown" before a Read that panics
func TestReadFromPanicReader(t *testing.T) {

	// First verify non-panic behaviour
	var buf Buffer
	i, err := buf.ReadFrom(panicReader{})
	if err != nil {
		t.Fatal(err)
	}
	if i != 0 {
		t.Fatalf("unexpected return from bytes.ReadFrom (1): got: %d, want %d", i, 0)
	}

		}
		if (formats != FormatUnknown) && (err != nil) {
			t.Errorf("test %d, unexpected error: %v", i, err)
		}
		if (formats == FormatUnknown) && (err == nil) {
			t.Errorf("test %d, got nil-error, want non-nil error", i)
		}
	}
}

func Benchmark(b *testing.B) {
	type file struct {
		hdr  *Header
		body []byte
	}

	vectors := []struct {
		label string
		files []file
	}{{

      for (int i = 0; i < params.size(); i++) {
        TypeToken<?> paramType = type.resolveType(typeVars[i]);
        // We require all @Generates methods to either be parameter-less or accept non-null
        // values for their generic parameter types.
        Object argValue = generate(paramType);
        if (argValue == null) {
          // When a parameter of a @Generates method cannot be created,

 * subtype of {@code Network} that provides methods for adding and removing nodes and edges. If you
 * do not need to mutate a network (e.g. if you write a method than runs a read-only algorithm on
 * the network), you should use the non-mutating {@link Network} interface, or an {@link
 * ImmutableNetwork}.
 *
 * <p>You can create an immutable copy of an existing {@code Network} using {@link
 * ImmutableNetwork#copyOf(Network)}:
 *
 * <pre>{@code

i»ÊufsqKó»\aLycx+ }Dš ŸeSbûUô}rQñ`¾™È¬i_\yUÖ ­yûZ_s¬Y}PÚqJ ‘rGtãjÒÀÀ~^>dg £ÉÒ»³ DqÐ.cW%1ò^¼]ÜKFVÕúMI),éærsL}G~Ü„\WQV¦MDCfw@Üe ô}°j÷inS{Gpucò’}S\eUuð÷ xH2 q# ò; 6Ä 3 Ä" %è> 2 5 *\ wKûryˆtXI ‰åt † ixÉkfs¦¾ÿx mmFwfðsfˆYïN©æiIIF„{æV oܤBH_ _KÖ„lYqfpª]ò^ñŒ c` ‘„?mPJmRªª° *Àt·ÆKut¸drikÍïD¬ÖmlX_ò ø ò¦V\G²tcS^ˆ}Eåpeòs¾tñ°†TVÜxž»1Áö ÔÒî ¾ ª ¸€š úqn>þôWc„Wx„ÙkB^ˆÄøG°\ñ+N¼7 ï*qnze{~Õ¨¢ ñS¢w{òˆAÕC¢Djg¢.\ñqEeÖ¦Bm¼oPGqBs NAåU VÕ’ª{|~UÿìÊWlþGuVÕ´ú1.>þ 0;=9%Ä" øeñx@C`xK¼k`¼tøC¢3ip ªÕïTFgÖ”þÔ-cS’åKbªãTqo Wëptioz†MQcxé°}Tc¼7...

      status->status = InvalidArgument("Unparseable RunOptions proto");
      return;
    }
    if (run_metadata != nullptr && run_metadata->data != nullptr) {
      status->status =
          InvalidArgument("Passing non-empty run_metadata is invalid.");
      return;
    }

    RunMetadata run_metadata_proto;
    result = session->Run(run_options_proto, input_pairs, output_tensor_names,