int bytes = dynamic_buffer.WriteToBuffer(&tensor_buffer);
    if (use_buffer_offset_) {
      std::vector<uint8_t> buffer_data(tensor_buffer, tensor_buffer + bytes);
      free(tensor_buffer);
      buffer_data_map_[index] =
          std::string(buffer_data.begin(), buffer_data.end());
      return tflite::CreateBuffer(builder_, 0, 1, 1);
    } else {
      if (IsModelBiggerThan2GB(bytes)) {

                if ( bufferOffset % 8 != 0 ) {
                    throw new PACDecodingException("Unaligned buffer " + bufferType);
                }

                byte[] bufferData = new byte[bufferSize];
                System.arraycopy(data, (int) bufferOffset, bufferData, 0, bufferSize);

                switch ( bufferType ) {
                case PacConstants.LOGON_INFO:
                    // PAC Credential Information

      split_logs_by_tag false
      # Set the buffer type to file to improve the reliability and reduce the memory consumption
      buffer_type file
      buffer_path /var/log/fluentd-buffers/kubernetes.containers.buffer
      # Set queue_full action to block because we want to pause gracefully
      # in case of the off-the-limits load instead of throwing an exception

      split_logs_by_tag false
      # Set the buffer type to file to improve the reliability and reduce the memory consumption
      buffer_type file
      buffer_path /var/log/fluentd-buffers/kubernetes.containers.buffer
      # Set queue_full action to block because we want to pause gracefully
      # in case of the off-the-limits load instead of throwing an exception

  # Allow log entries from multiple containers to be sent in the same request.
  split_logs_by_tag false
  # Set the buffer type to file to improve the reliability and reduce the memory consumption
  buffer_type file
  buffer_path /var/log/fluentd-buffers/kubernetes.containers.buffer
  # Set queue_full action to block because we want to pause gracefully
  # in case of the off-the-limits load instead of throwing an exception