code: def process_prompts(self, prompt):
      from nemo.collections.multimodal.data.neva.neva_dataset import (
          DEFAULT_IMAGE_TOKEN,
          preprocess_llama_2,
          preprocess_multimodal,
          preprocess_nvgpt,
          preprocess_v1,
      )
  
      list_data_dict = []
      if self.multimodal_cfg["conv_template"] == "nvgpt":
          record = {
              "system": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n",
              "conversations": [
                  {
                      "from": "User",
                      "value": prompt,
                  },
                  {
                      "from": "Assistant",
                      "value": "",
                      "label": "quality:6,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:6,not_appropriate:0",
                  },
              ],
          }
  
          for turn in record["conversations"]:  #
              if turn.get("value") is not None:
                  turn["value"] = re.sub(
                      "<image>",
                      f"{DEFAULT_IMAGE_TOKEN}\n",
                      turn["value"],
                  )
          list_data_dict.append(record)
  
          sources = preprocess_multimodal(
              copy.deepcopy(list_data_dict),
              self.multimodal_cfg,
              self.num_media_latents,
          )  # HARDCODED FOR NOW
          data_dict = preprocess_nvgpt(
              sources, self.tokenizer, self.multimodal_cfg
          )
  
      elif self.multimodal_cfg["conv_template"] == "llama_2":
          record = {
              "conversations": [
                  {
                      "from": "human",
                      "value": prompt,
                  },
                  {
                      "from": "gpt",
                      "value": "",
                  },
              ],
          }
  
          for turn in record["conversations"]:
              if turn.get("value") is not None:
                  turn["value"] = re.sub(
                      "<image>",
                      f"{DEFAULT_IMAGE_TOKEN}\n",
                      turn["value"],
                  )
          list_data_dict.append(record)
  
          sources = preprocess_multimodal(
              copy.deepcopy(list_data_dict),
              self.multimodal_cfg,
              self.num_media_latents,
          )  # HARDCODED FOR NOW
          data_dict = preprocess_llama_2(
              sources, self.tokenizer, self.multimodal_cfg
          )
      elif self.multimodal_cfg["conv_template"] == "v1":
          record = {
              "conversations": [
                  {
                      "from": "human",
                      "value": prompt,
                  },
                  {
                      "from": "gpt",
                      "value": "",
                  },
              ],
          }
  
          for turn in record["conversations"]:
              if turn.get("value") is not None:
                  turn["value"] = re.sub(
                      "<image>",
                      f"{DEFAULT_IMAGE_TOKEN}\n",
                      turn["value"],
                  )
          list_data_dict.append(record)
  
          sources = preprocess_multimodal(
              copy.deepcopy(list_data_dict),
              self.multimodal_cfg,
              self.num_media_latents,
          )  # HARDCODED FOR NOW
          data_dict = preprocess_v1(
              sources, self.tokenizer, self.multimodal_cfg
          )
      else:
          raise ValueError(
              f"Conversation template `{self.conv_template}` is not supported in Neva now."
          )
      return data_dict["tokens"].tolist()
  
imports: import abc
  import copy
  import os
  import re
  import warnings
  from typing import List, Set, Tuple
  import torch
  from transformers import CLIPImageProcessor
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
label_desc: Use image node_id 4 for calling the NevaModelTextGenerationStrategy obj's underlying member method code with example usage: obj.process_prompts(prompt) and returns: data_dict