code: def run_inference(args):
      """
      Run inference on ActivityNet QA DataSet using the Video-ChatGPT model.
  
      Args:
          args: Command-line arguments.
      """
      # Initialize the model
      model_name = get_model_name_from_path(args.model_path)
      (
          tokenizer,
          model,
          image_processor,
          context_len,
      ) = load_pretrained_model(
          args.model_path,
          args.model_base,
          model_name,
          args.model_max_length,
      )
  
      # Load both ground truth file containing questions and answers
      with open(args.gt_file) as file:
          gt_questions = json.load(file)
      gt_questions = get_chunk(
          gt_questions, args.num_chunks, args.chunk_idx
      )
  
      # Create the output directory if it doesn't exist
      if not os.path.exists(args.output_dir):
          os.makedirs(args.output_dir)
  
      video_formats = [".mp4", ".avi", ".mov", ".mkv"]
      if args.num_chunks > 1:
          output_name = f"{args.num_chunks}_{args.chunk_idx}"
      else:
          output_name = args.output_name
      answers_file = os.path.join(
          args.output_dir, f"{output_name}.json"
      )
      ans_file = open(answers_file, "w")
  
      for sample in tqdm(gt_questions):
          video_name = sample["video_id"]
          question = sample["question"]
          id = sample["id"]
          answer = sample["answer"]
  
          sample_set = {
              "id": id,
              "question": question,
              "answer": answer,
          }
  
          # Load the video file
          for fmt in video_formats:  # Added this line
              temp_path = os.path.join(
                  args.video_dir, f"{video_name}{fmt}"
              )
              if os.path.exists(temp_path):
                  video_path = temp_path
                  break
  
          # Check if the video exists
          if os.path.exists(video_path):
              video = load_video(video_path)
              video = (
                  image_processor.preprocess(
                      video, return_tensors="pt"
                  )["pixel_values"]
                  .half()
                  .cuda()
              )
              video = [video]
  
          # try:
          # Run inference on the video and add the output to the list
  
          qs = question
          if model.config.mm_use_im_start_end:
              qs = (
                  DEFAULT_IM_START_TOKEN
                  + DEFAULT_IMAGE_TOKEN
                  + DEFAULT_IM_END_TOKEN
                  + "\n"
                  + qs
              )
          else:
              qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
  
          conv = conv_templates[args.conv_mode].copy()
          conv.append_message(conv.roles[0], qs)
          conv.append_message(conv.roles[1], None)
          prompt = conv.get_prompt()
  
          input_ids = (
              tokenizer_image_token(
                  prompt,
                  tokenizer,
                  IMAGE_TOKEN_INDEX,
                  return_tensors="pt",
              )
              .unsqueeze(0)
              .cuda()
          )
  
          stop_str = (
              conv.sep
              if conv.sep_style != SeparatorStyle.TWO
              else conv.sep2
          )
          keywords = [stop_str]
          stopping_criteria = KeywordsStoppingCriteria(
              keywords, tokenizer, input_ids
          )
  
          cur_prompt = question
          with torch.inference_mode():
              model.update_prompt([[cur_prompt]])
              output_ids = model.generate(
                  input_ids,
                  images=video,
                  do_sample=True,
                  temperature=0.2,
                  max_new_tokens=1024,
                  use_cache=True,
                  stopping_criteria=[stopping_criteria],
              )
  
          input_token_len = input_ids.shape[1]
          n_diff_input_output = (
              (input_ids != output_ids[:, :input_token_len])
              .sum()
              .item()
          )
          if n_diff_input_output > 0:
              print(
                  f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids"
              )
          outputs = tokenizer.batch_decode(
              output_ids[:, input_token_len:], skip_special_tokens=True
          )[0]
          outputs = outputs.strip()
          if outputs.endswith(stop_str):
              outputs = outputs[: -len(stop_str)]
          outputs = outputs.strip()
  
          sample_set["pred"] = outputs
          ans_file.write(json.dumps(sample_set) + "\n")
          ans_file.flush()
  
      ans_file.close()
  
file_path: llama-vid/llamavid/eval/model_msvd_qa.py
imports: import argparse
  import torch
  from llamavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
  from llamavid.conversation import conv_templates, SeparatorStyle
  from llamavid.model.builder import load_pretrained_model
  from llava.utils import disable_torch_init
  from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
  import json
  import os
  import math
  from tqdm import tqdm
  from decord import VideoReader, cpu
label_desc: Use image node_id 5 for calling a global function with example usage: run_inference(args) without return types