code: def predict(
      model,
      model_path,
      transforms,
      image_list,
      image_dir=None,
      trimap_list=None,
      save_dir="output",
      fg_estimate=True,
  ):
      """
      predict and visualize the image_list.
  
      Args:
          model (nn.Layer): Used to predict for input image.
          model_path (str): The path of pretrained model.
          transforms (transforms.Compose): Preprocess for input image.
          image_list (list): A list of image path to be predicted.
          image_dir (str, optional): The root directory of the images predicted. Default: None.
          trimap_list (list, optional): A list of trimap of image_list. Default: None.
          save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
      """
      utils.utils.load_entire_model(model, model_path)
      model.eval()
      nranks = paddle.distributed.get_world_size()
      local_rank = paddle.distributed.get_rank()
      if nranks > 1:
          img_lists = partition_list(image_list, nranks)
          trimap_lists = (
              partition_list(trimap_list, nranks)
              if trimap_list is not None
              else None
          )
      else:
          img_lists = [image_list]
          trimap_lists = (
              [trimap_list] if trimap_list is not None else None
          )
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(
          target=len(img_lists[0]), verbose=1
      )
      preprocess_cost_averager = TimeAverager()
      infer_cost_averager = TimeAverager()
      postprocess_cost_averager = TimeAverager()
      batch_start = time.time()
      with paddle.no_grad():
          for i, im_path in enumerate(img_lists[local_rank]):
              preprocess_start = time.time()
              trimap = (
                  trimap_lists[local_rank][i]
                  if trimap_list is not None
                  else None
              )
              data = preprocess(
                  img=im_path, transforms=transforms, trimap=trimap
              )
              preprocess_cost_averager.record(
                  time.time() - preprocess_start
              )
  
              infer_start = time.time()
              result = model(data)
              infer_cost_averager.record(time.time() - infer_start)
  
              postprocess_start = time.time()
              if isinstance(result, paddle.Tensor):
                  alpha = result
                  fg = None
              else:
                  alpha = result["alpha"]
                  fg = result.get("fg", None)
  
              alpha = reverse_transform(alpha, data["trans_info"])
              alpha = (alpha.numpy()).squeeze()
              alpha = (alpha * 255).astype("uint8")
              if fg is not None:
                  fg = reverse_transform(fg, data["trans_info"])
                  fg = (fg.numpy()).squeeze().transpose((1, 2, 0))
                  fg = (fg * 255).astype("uint8")
  
              # get the saved name
              if image_dir is not None:
                  im_file = im_path.replace(image_dir, "")
              else:
                  im_file = os.path.basename(im_path)
              if im_file[0] == "/" or im_file[0] == "\\":
                  im_file = im_file[1:]
  
              save_path = os.path.join(save_dir, im_file)
              mkdir(save_path)
              fg = save_result(
                  alpha,
                  save_path,
                  im_path=im_path,
                  trimap=trimap,
                  fg_estimate=fg_estimate,
                  fg=fg,
              )
  
              # rvm have member which need to reset.
              if hasattr(model, "reset"):
                  model.reset()
  
              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()
              if local_rank == 0:
                  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()
      return alpha, fg
  
file_path: PaddleSeg/Matting/ppmatting/core/predict.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
label_desc: Use image node_id 5 for calling a global function with example usage: predict(model, model_path, transforms, image_list, image_dir, trimap_list, save_dir, fg_estimate) and returns: alpha,  fg