case tensorflow::AttrValue::kList: {
      // String
      if (const int s_size = default_value.list().s_size()) {
        absl::InlinedVector<const void*, 4> values_vector;
        values_vector.reserve(s_size);
        absl::InlinedVector<size_t, 4> lengths_vector;
        lengths_vector.reserve(s_size);
        for (int i = 0; i < s_size; ++i) {
          const string& v = default_value.list().s(i);

  return list.s_size() == 0 && list.i_size() == 0 && list.f_size() == 0 &&
         list.b_size() == 0 && list.type_size() == 0 &&
         list.shape_size() == 0 && list.tensor_size() == 0;
}

string ToCamelCase(StringPiece str) {
  string result;
  const char joiner = '_';
  size_t i = 0;
  bool cap = true;
  while (i < str.size()) {
    const char c = str[i++];
    if (c == '>') {

  if (attr->list().field##_size() > 0) {      \
    *list_size = attr->list().field##_size(); \
    __VA_ARGS__;                              \
    break;                                    \
  }

      LIST_CASE(
          s, TF_ATTR_STRING, *total_size = 0;
          for (int i = 0; i < attr->list().s_size();
               ++i) { *total_size += attr->list().s(i).size(); });
      LIST_CASE(i, TF_ATTR_INT);

                            JAC_T* max_error) {
  if (xs.size() != x_shapes.size()) {
    return errors::InvalidArgument("xs(size ", xs.size(),
                                   ") and x_shapes(size ", x_shapes.size(),
                                   ") must be the same size.");
  }
  if (ys.size() != y_shapes.size()) {
    return errors::InvalidArgument("ys(size ", ys.size(),

                            std::vector<TfLiteDimensionType>* format,
                            std::vector<int>* b_map, std::vector<int>* b_size) {
  const int dims_count = block_size.size();
  traversal_order->resize(dims_count);
  format->resize(dims_count);
  for (int i = 0; i < dims_count; i++) {
    (*traversal_order)[i] = i;
  }
  for (int i = 0; i < dims_count - 1; i++) {

        public String remark;
        public int permissions;
        public int max_uses;
        public int current_uses;
        public String path;
        public String password;
        public int sd_size;
        public byte[] security_descriptor;

        public void encode(NdrBuffer _dst) throws NdrException {
            _dst.align(4);
            _dst.enc_ndr_referent(netname, 1);

		intRegs += uint64(typ.intRegs)
		floatRegs += uint64(typ.floatRegs)
	}

	// Final size includes trailing padding.
	size = RoundUp(size, int64(maxAlign))

	if intRegs > math.MaxUint8 || floatRegs > math.MaxUint8 {
		intRegs = math.MaxUint8
		floatRegs = math.MaxUint8
	}

	t.width = size
	t.align = maxAlign
	t.intRegs = uint8(intRegs)
	t.floatRegs = uint8(floatRegs)

        public String remark;
        public int permissions;
        public int max_uses;
        public int current_uses;
        public String path;
        public String password;
        public int sd_size;
        public byte[] security_descriptor;


        @Override
        public void encode ( NdrBuffer _dst ) throws NdrException {
            _dst.align(4);
            _dst.enc_ndr_referent(this.netname, 1);

  } else {
    const void* address =
        mmap(nullptr, st.st_size, PROT_READ, MAP_PRIVATE, fd, 0);
    if (address == MAP_FAILED) {
      TF_SetStatusFromIOError(status, errno, path);
    } else {
      region->plugin_memory_region =
          new tf_read_only_memory_region::PosixMemoryRegion{
              address, static_cast<uint64_t>(st.st_size)};
      TF_SetStatus(status, TF_OK, "");
    }
  }

	w.codegenEncoding.generate(w.codegenFreq[:], 7)
	size, numCodegens := w.dynamicSize(w.literalEncoding, w.offsetEncoding, 0)

	// Store bytes, if we don't get a reasonable improvement.
	if ssize, storable := w.storedSize(input); storable && ssize < (size+size>>4) {
		w.writeStoredHeader(len(input), eof)
		w.writeBytes(input)
		return
	}

	// Write Huffman table.