code: def predict_video(
      model,
      model_path,
      transforms,
      video_path,
      save_dir="output",
      fg_estimate=True,
  ):
      """
      predict and visualize the video.
  
      Args:
          model (nn.Layer): Used to predict for input video.
          model_path (str): The path of pretrained model.
          transforms (transforms.Compose): Preprocess for frames of video.
          video_path (str): the video path to be predicted.
          save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
          fg_estimate (bool, optional): Whether to estimate foreground when predicting. It is invalid if the foreground is predicted by model. Default: True
      """
      utils.utils.load_entire_model(model, model_path)
      model.eval()
  
      # Build loader and writer for video
      loader, writers = build_loader_writter(
          video_path, transforms, save_dir=save_dir
      )
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(target=len(loader), verbose=1)
      preprocess_cost_averager = TimeAverager()
      infer_cost_averager = TimeAverager()
      postprocess_cost_averager = TimeAverager()
      batch_start = time.time()
      with paddle.no_grad():
          for i, data in enumerate(loader):
              preprocess_cost_averager.record(time.time() - batch_start)
  
              infer_start = time.time()
              result = model(data)  # result maybe a Tensor or a dict
              if isinstance(result, paddle.Tensor):
                  alpha = result
                  fg = None
              else:
                  alpha = result["alpha"]
                  fg = result.get("fg", None)
              infer_cost_averager.record(time.time() - infer_start)
  
              postprocess_start = time.time()
              postprocess(
                  fg,
                  alpha,
                  data["ori_img"],
                  trans_info=data["trans_info"],
                  writers=writers,
                  fg_estimate=fg_estimate,
              )
              postprocess_cost_averager.record(
                  time.time() - postprocess_start
              )
  
              preprocess_cost = preprocess_cost_averager.get_average()
              infer_cost = infer_cost_averager.get_average()
              postprocess_cost = postprocess_cost_averager.get_average()
              progbar_pred.update(
                  i + 1,
                  [
                      ("preprocess_cost", preprocess_cost),
                      ("infer_cost cost", infer_cost),
                      ("postprocess_cost", postprocess_cost),
                  ],
              )
  
              preprocess_cost_averager.reset()
              infer_cost_averager.reset()
              postprocess_cost_averager.reset()
              batch_start = time.time()
      if hasattr(model, "reset"):
          model.reset()
      loader.dataset.release()
      for k, v in writers.items():
          v.release()
  
file_path: PaddleSeg/Matting/ppmatting/core/predict_video.py
imports: import os
  import math
  import time
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.core import infer
  from paddleseg.utils import logger, progbar, TimeAverager
  from ppmatting.utils import mkdir, estimate_foreground_ml, VideoReader, VideoWriter
label_desc: Use image node_id 4 for calling a global function with example usage: predict_video(model, model_path, transforms, video_path, save_dir, fg_estimate) without return types