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 == '>') {

      return std::to_string(attr_value.f());
    case AttrValue::kB:
      return attr_value.b() ? "true" : "false";
    case AttrValue::kList:
      if (attr_.full_type() == "list(string)" &&
          attr_value.list().s_size() == 0) {
        return "{}";
      }
      LOG(WARNING) << "Unimplemented: default value of list-typed attribute.";
      return "/* UNIMPLEMENTED */";
    case AttrValue::kShape:
    case AttrValue::kTensor:

  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);

  DCHECK(tf_results->missing_unused_key_names_data.empty());

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);
    tf_results->missing_unused_key_names[i] =

                            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(),

	sh_addr      uint32 /* Section virtual addr at execution */
	sh_offset    uint32 /* Section file offset */
	sh_size      uint32 /* Section size in bytes */
	sh_link      uint32 /* Link to another section */
	sh_info      uint32 /* Additional section information */
	sh_addralign uint32 /* Section alignment */
	sh_entsize   uint32 /* Entry size if section holds table */
}

type elfDyn struct {
	d_tag int32  /* Dynamic entry type */

	sem_type  uint16
	sem_magic uint16
	sem_pad1  [3]uint64
	sem_pad2  [2]uint64
}

type sigset struct {
	__sigbits [4]uint32
}

type stackt struct {
	ss_sp     *byte
	ss_size   uintptr
	ss_flags  int32
	pad_cgo_0 [4]byte
}

type siginfo struct {
	si_signo int32
	si_code  int32
	si_errno int32
	si_pad   int32
	__data   [240]byte
}

                          << ") != terminator operands ("
                          << term->getNumOperands() << ")";
    return {};
  }
  // Verify number of tensors for inputs and outputs matches size
  // of the list in the signature def.
  if (input_names.size() != sig_def_inputs.size() ||
      output_names.size() != sig_def_outputs.size()) {
    main_op.emitWarning(

    ```

    If kernel\_size % block\_size != 0, padding is needed for the weight before
    transform, input of Convolution needs to be padded as well.

1.  Rewrite the first convolution

    Need to rewrite the first convolution's shape of input from [batch\_size,
    height, width, 3] to [batch\_size, height // block\_size, width //
    block\_size, 3 \* block\_size \* block\_size] and kernel shape from