Len   uint32
	Insns *BpfInsn
}

type BpfInsn struct {
	Code uint16
	Jt   uint8
	Jf   uint8
	K    uint32
}

type BpfHdr struct {
	Tstamp  Timeval32
	Caplen  uint32
	Datalen uint32
	Hdrlen  uint16
	_       [2]byte
}

type Termios struct {
	Iflag  uint64
	Oflag  uint64
	Cflag  uint64
	Lflag  uint64
	Cc     [20]uint8
	Ispeed uint64
	Ospeed uint64
}

    self.assertTrue(
        file_io.file_exists_v2(
            os.path.join(self._output_saved_model_path, asset_filename)
        )
    )

  # tf.data.Dataset is as an Iterable (thus can be used as representative
  # dataset) only in TF2 (eager mode).
  @test_util.run_v2_only
  def test_model_ptq_use_tf_dataset_for_representative_dataset(self):
    self._create_matmul_model(
        input_shape=(1, 1024),

	WeakBindOff, WeakBindLen uint32
	LazyBindOff, LazyBindLen uint32
	ExportOff, ExportLen     uint32
}

type linkEditDataCmd struct {
	Cmd              macho.LoadCmd
	Len              uint32
	DataOff, DataLen uint32
}

type encryptionInfoCmd struct {
	Cmd                macho.LoadCmd
	Len                uint32
	CryptOff, CryptLen uint32
	CryptId            uint32
}

type uuidCmd struct {
	Cmd  macho.LoadCmd

  // pass to merge these together into the earliest copy.
  llvm::SetVector<int64_t> forward_variable_inputs;

  // Validate that the only resource inputs that are read by ops in
  // forward_pass_ops are dataset and variable ops.
  int64_t resource_count = 0;
  for (auto argument : loop_body_func.getArguments()) {
    // Check that all resource arguments are either fed to iterator get next

			listSym.WriteCURelativeAddr(ctxt, listSym.Size, startPC, int64(end))
		}

		i += 2 * ctxt.Arch.PtrSize
		datalen := 2 + int(ctxt.Arch.ByteOrder.Uint16(list[i:]))
		listSym.WriteBytes(ctxt, listSym.Size, list[i:i+datalen]) // copy datalen and location encoding
		i += datalen
	}

	// Location list contents, now with real PCs.
	// End entry.
	listSym.WriteInt(ctxt, listSym.Size, ctxt.Arch.PtrSize, 0)

}

// calculateSignedChunkLength - calculates the length of the overall stream (data + metadata)
func calculateStreamContentLength(dataLen, chunkSize int64) int64 {
	if dataLen <= 0 {
		return 0
	}
	chunksCount := dataLen / chunkSize
	remainingBytes := dataLen % chunkSize
	var streamLen int64
	streamLen += chunksCount * calculateSignedChunkLength(chunkSize)
	if remainingBytes > 0 {

      DenseIntElementsAttr index_attr;
      if (!matchPattern(index, m_Constant(&index_attr))) return failure();
      indices.push_back(index_attr);

      RankedTensorType data_ty =
          mlir::dyn_cast<RankedTensorType>(data.getType());
      if (!data_ty || !data_ty.hasStaticShape()) return failure();
    }

    // Compute type of each of the items and shape to use while reshaping inputs

					<keyring>${gpg.keyring}</keyring>
					key>${gpg.key}</key>
					<passphrase>${gpg.passphrase}</passphrase>
					<signMethod>${deb.sign.method}</signMethod>
					-->
					<dataSet>
						<!-- Add app directory -->
						<data>
							<type>directory</type>
							<src>${project.build.directory}/fess</src>
							<excludes>**/fess_config.properties,**/fess_env*.properties,**/tika.xml</excludes>

def TF_FlatMapDatasetOp : TF_Op<"FlatMapDataset", [Pure]> {
  let summary = [{
Creates a dataset that applies `f` to the outputs of `input_dataset`.
  }];

  let description = [{
Unlike MapDataset, the `f` in FlatMapDataset is expected to return a
Dataset variant, and FlatMapDataset will flatten successive results
into a single Dataset.
  }];

  let arguments = (ins
    TF_VariantTensor:$input_dataset,

      maxval: float = 1.0,
      dtype: dtypes.DType = dtypes.float32,
      num_examples: int = 8,
  ) -> repr_dataset.RepresentativeDataset:
    """Creates a data generator to be used as representative dataset.

    Supports generating random value input tensors mapped by the `input_key`.

    Args:
      input_key: The string key that identifies the created tensor as an input.
      shape: Shape of the tensor data.