self._emit_with_loc(
              '\n{} = shape.rank {} : !shape.shape -> !shape.size'.format(
                  len_value, arg), node)
          size_value = self._ssa_name('len_size')
          self._emit_with_loc(
              '\n{} = shape.size_to_index {} : !shape.size'.format(
                  size_value, len_value), node)
        elif ty == TFRTypes.TENSOR_LIST:

func rangesMIMESize(ranges []httpRange, contentType string, contentSize int64) (encSize int64) {
	var w countingWriter
	mw := multipart.NewWriter(&w)
	for _, ra := range ranges {
		mw.CreatePart(ra.mimeHeader(contentType, contentSize))
		encSize += ra.length
	}
	mw.Close()
	encSize += int64(w)
	return
}

func sumRangesSize(ranges []httpRange) (size int64) {

                                        s);
  }

  if (TF_GetCode(s) == TF_OK) {
    kernel->in_size = TF_DataTypeSize(kernel->input_data_type);
    kernel->out_size = TF_DataTypeSize(kernel->output_data_type);

    size_t check_size = std::max(kernel->in_size, kernel->out_size) %
                        std::min(kernel->in_size, kernel->out_size);
    if (check_size != 0) {
      std::ostringstream err;

        int i;

        if( len > ( pipe_buf.length - used )) {
            byte[] tmp;
            int new_size;

            new_size = pipe_buf.length * 2;
            if( len > ( new_size - used )) {
                new_size = len + used;
            }
            tmp = pipe_buf;
            pipe_buf = new byte[new_size];
            i = tmp.length - beg_idx;
            if( used > i ) { /* 2 chunks */

}

TF_Buffer* TF_CoordinationServiceGetKeyValue(const char* key, int64_t key_size,
                                             TF_CoordinationServiceAgent* agent,
                                             TF_Status* status) {
  auto* cc_agent = reinterpret_cast<tsl::CoordinationServiceAgent*>(agent);
  auto value = cc_agent->GetKeyValue(std::string_view(key, key_size));
  return ProcessGetKeyValueResult(value, status);
}

// nullptr will be returned.
TF_CAPI_EXPORT extern TF_Buffer* TF_CoordinationServiceGetKeyValue(
    const char* key, int64_t key_size, TF_CoordinationServiceAgent* agent,
    TF_Status* status);

TF_CAPI_EXPORT extern TF_Buffer* TF_CoordinationServiceGetKeyValueWithTimeout(
    const char* key, int64_t key_size, int64_t timeout_seconds,
    TF_CoordinationServiceAgent* agent, TF_Status* status);

    public MimeMap() throws IOException {
        int n;

        in = new byte[IN_SIZE];
        InputStream is = getClass().getClassLoader().getResourceAsStream( "jcifs/smb1/util/mime.map" );

        inLen = 0;
        while(( n = is.read( in, inLen, IN_SIZE - inLen )) != -1 ) {
            inLen += n;
        }
        if( inLen < 100 || inLen == IN_SIZE ) {

}

// construct table of powers of bb*leafSize to use in subdivisions.
func divisors(m int, b Word, ndigits int, bb Word) []divisor {
	// only compute table when recursive conversion is enabled and x is large
	if leafSize == 0 || m <= leafSize {
		return nil
	}

	// determine k where (bb**leafSize)**(2**k) >= sqrt(x)
	k := 1
	for words := leafSize; words < m>>1 && k < len(cacheBase10.table); words <<= 1 {
		k++

  variable->set_node_name("myvar_readonly");
  variable->mutable_shape()->add_dim()->set_size(1);
  variable->set_type(DT_FLOAT);
  variable->set_readonly(true);
  tf2xla::Variable* variable2 = config.add_variable();
  variable2->set_node_name("myvar");
  variable2->mutable_shape()->add_dim()->set_size(1);
  variable2->set_type(DT_FLOAT);
  tf2xla::Variable* variable3 = config.add_variable();

      }
      size += tensor_type.getNumElements() *
              tensor_type.getElementType().getIntOrFloatBitWidth();
    }
    return size;
  };

  int64_t results_size = get_size(inst->getResultTypes());
  int64_t operands_size = get_size(inst->getOperandTypes());

  constexpr int kSizeFactor = 2;
// TODO(b/233827625): Remove TF_DISABLE_CONSTANT_FOLDING macro.
#ifdef TF_DISABLE_CONSTANT_FOLDING