code: def process_text(
      self,
      ann,
      data_debug_path=None,
      data_debug_counter=0,
      first_message=False,
      num_images=1,
  ):
      images = ann["image_ids"]
      processed_images = {}
      conv_list = []
      # At least one conversation from llava
      for idx in range(len(images)):
          img_key = images[idx]
          if img_key in self.ann_llava:
              conversations = self.ann_llava[img_key]
              min_num_draw = (
                  1 if idx < (len(images) - 1) else 0
              )  # The last image could have 0 llava conversation since it won't break image order
              num_draw = random.randint(
                  min_num_draw, len(conversations)
              )
              chosen = random.sample(
                  list(range(len(conversations))), num_draw
              )
              for cid in chosen:
                  conv = conversations[cid]
                  num_convs = len(conv) // 2
                  for conv_id in range(num_convs):
                      question = conv[int(2 * conv_id)]["value"]
                      # remove '<image>' tag and '\n'
                      with_image = img_key not in processed_images
                      question = question.replace(
                          "<image>", ""
                      ).replace("\n", "")
                      answer = conv[int(2 * conv_id + 1)]["value"]
                      instruction = self.prompter(
                          question,
                          with_image=with_image,
                          first_message=(
                              len(conv_list) == 0 and first_message
                          ),
                      )
                      if with_image:
                          instruction = (
                              self.post_process_text_image_count(
                                  instruction,
                                  1,
                                  offset=len(processed_images),
                              )
                          )
                      single_conv = dict(
                          instruction=instruction, answer=answer
                      )
                      conv_list.append(single_conv)
                      processed_images[img_key] = 1
  
      # At least one conversation from otter
      question_list = ann["instruction"]
      answer_list = ann["answer"]
      num_convs = len(question_list)
      num_draw = random.randint(1, num_convs)
      chosen = random.sample(list(range(num_convs)), num_draw)
      for cid in chosen:
          question = question_list[cid]
          # remove '<image>' tag and '\n'
          question = question.replace("<image>", "").replace("\n", "")
          answer = answer_list[cid]
          num_images = len(images) - len(processed_images)
          instruction = self.prompter(
              question,
              with_image=(num_images > 0),
              first_message=(len(conv_list) == 0),
              num_images=num_images,
          )
          if num_images > 0:
              instruction = self.post_process_text_image_count(
                  instruction, num_images, offset=len(processed_images)
              )
          single_conv = dict(instruction=instruction, answer=answer)
          conv_list.append(single_conv)
          processed_images = images
      # Follow-up llava conversations
      if self.followup:
          image_tags = {
              0: [
                  "In image 1, ",
                  "In image a, ",
                  "In the first image, ",
              ],
              1: [
                  "In image 2, ",
                  "In image b, ",
                  "In the second image, ",
              ],
          }
          for idx in range(len(images)):
              img_key = images[idx]
              if img_key in self.ann_llava:
                  conversations = self.ann_llava[img_key]
                  # min_num_draw = 1
                  # num_draw = random.randint(min_num_draw, len(conversations))
                  num_draw = 1  # To avoid making too complex conversation, we limit num of follow-up conversation to 1 per image
                  chosen = random.sample(
                      list(range(len(conversations))), num_draw
                  )
                  for cid in chosen:
                      conv = conversations[cid]
                      num_convs = len(conv) // 2
                      for conv_id in range(num_convs):
                          question = conv[int(2 * conv_id)]["value"]
                          # remove '<image>' tag and '\n'
                          question = question.replace(
                              "<image>", ""
                          ).replace("\n", "")
                          answer = conv[int(2 * conv_id + 1)]["value"]
                          # Add image tags so the model knows which image we are referring
                          chosen_tag = random.choice(image_tags[idx])
                          question = (
                              chosen_tag
                              + question[0].lower()
                              + question[1:]
                          )
                          answer = (
                              chosen_tag
                              + answer[0].lower()
                              + answer[1:]
                          )
                          instruction = self.prompter(
                              question,
                              with_image=False,
                              first_message=False,
                          )
                          single_conv = dict(
                              instruction=instruction, answer=answer
                          )
                          conv_list.append(single_conv)
      save_debug_text(
          conv_list, data_debug_path, data_debug_counter, get_rank()
      )
      return conv_list
  
file_path: deepspeedexamples/applications/DeepSpeed-VisualChat/utils/data/llava_otter_blend_dataset.py
imports: import os
  import torch
  import json
  import random
  from tqdm import tqdm
  from PIL import Image
  from .vqa_dataset import VQADataset
  from utils.utils import print_rank_0, is_rank_0, get_rank
  from .utils import save_debug_image, save_debug_text
label_desc: Use image node_id 5 for calling the LlavaOtterBlendDataset obj's underlying member method code with example usage: obj.process_text(ann, data_debug_path, data_debug_counter, first_message, num_images) and returns: conv_list