"12.1.9 UNIMPLEMENTED"
"12.2.1 UNIMPLEMENTED"
"12.2.10 UNIMPLEMENTED"
"12.2.11 UNIMPLEMENTED"
"12.2.12 UNIMPLEMENTED"
"12.2.13 UNIMPLEMENTED"
"12.2.14 UNIMPLEMENTED"
"12.2.15 UNIMPLEMENTED"
"12.2.16 UNIMPLEMENTED"
"12.2.17 UNIMPLEMENTED"
"12.2.18 UNIMPLEMENTED"
"12.2.2 UNIMPLEMENTED"
"12.2.3 UNIMPLEMENTED"
"12.2.4 UNIMPLEMENTED"
"12.2.5 UNIMPLEMENTED"
"12.2.6 UNIMPLEMENTED"
"12.2.7 UNIMPLEMENTED"
"12.2.8 UNIMPLEMENTED"

TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
                           TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Creating while loops is not supported on mobile. File a bug at "
      "https://github.com/tensorflow/tensorflow/issues if this feature is "
      "important to you");
  return EmptyWhileParams();
#else

    void testReadBytesWireFormatNoop() {
        byte[] buffer = new byte[10];
        int used = cmd.readBytesWireFormat(buffer, 0);
        assertEquals(0, used, "readBytesWireFormat is unimplemented and must return 0");
    }

    private static byte[] subArray(byte[] src, int offset, int length) {
        byte[] dst = new byte[length];
        System.arraycopy(src, offset, dst, 0, length);

            break;
        case Smb2Constants.SMB2_DIALECT_0300:
        case Smb2Constants.SMB2_DIALECT_0302:
            this.signingKey = Smb3KeyDerivation.deriveSigningKey(dialect, sessionKey, new byte[0] /* unimplemented */);
            this.algorithmName = "AESCMAC";
            this.provider = Crypto.getProvider();
            break;
        case Smb2Constants.SMB2_DIALECT_0311:
            if (preauthIntegrityHash == null) {

   * getAnnotatedBounds()} method, then its return type means it won't compile on Android, while if
   * we don't include the method, then the compiler will complain that an abstract method is
   * unimplemented. So instead we use a dynamic proxy to get an implementation. If the method being
   * called on the {@code TypeVariable} instance has the same name as one of the public methods of

TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "PluggableDevice plugin functionality is not supported on mobile");
  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);

   * getAnnotatedBounds()} method, then its return type means it won't compile on Android, while if
   * we don't include the method, then the compiler will complain that an abstract method is
   * unimplemented. So instead we use a dynamic proxy to get an implementation. If the method being
   * called on the {@code TypeVariable} instance has the same name as one of the public methods of

   * @return the converted instance; <b>must not</b> be null
   * @throws UnsupportedOperationException if backward conversion is not implemented; this should be
   *     very rare. Note that if backward conversion is not only unimplemented but
   *     unimplement<i>able</i> (for example, consider a {@code Converter<Chicken, ChickenNugget>}),
   *     then this is not logically a {@code Converter} at all, and should just implement {@link
   *     Function}.

		} else {
			r1, r2, scale, ok = p.register(tok.String(), 0)
			if !ok {
				p.errorf("indirect through non-register %s", tok)
			}
			if r2 != 0 {
				p.errorf("unimplemented two-register form")
			}
			a.Index = r1
			if scale != 0 && scale != 1 && (p.arch.Family == sys.ARM64 ||
				p.arch.Family == sys.PPC64) {
				// Support (R1)(R2) (no scaling) and (R1)(R2*1).

  EXPECT_EQ(TF_OUT_OF_RANGE,
            static_cast<TF_Code>(tensorflow::error::OUT_OF_RANGE));
  EXPECT_EQ(TF_UNIMPLEMENTED,
            static_cast<TF_Code>(tensorflow::error::UNIMPLEMENTED));
  EXPECT_EQ(TF_INTERNAL, static_cast<TF_Code>(tensorflow::error::INTERNAL));
  EXPECT_EQ(TF_UNAVAILABLE,
            static_cast<TF_Code>(tensorflow::error::UNAVAILABLE));