code: def __init__(
      self,
      weight_path=None,
      config=None,
      image_size=256,
      relative=True,
      adapt_scale=False,
      find_best_frame=False,
      best_frame=None,
      face_detector="sfd",
      multi_person=False,
      face_enhancement=True,
      batch_size=1,
      mobile_net=False,
  ):
      if config is not None and isinstance(config, str):
          with open(config) as f:
              self.cfg = yaml.load(f, Loader=yaml.SafeLoader)
      elif isinstance(config, dict):
          self.cfg = config
      elif config is None:
          self.cfg = {
              "model": {
                  "common_params": {
                      "num_kp": 10,
                      "num_channels": 3,
                      "estimate_jacobian": True,
                  },
                  "generator": {
                      "kp_detector_cfg": {
                          "temperature": 0.1,
                          "block_expansion": 32,
                          "max_features": 1024,
                          "scale_factor": 0.25,
                          "num_blocks": 5,
                      },
                      "generator_cfg": {
                          "block_expansion": 64,
                          "max_features": 512,
                          "num_down_blocks": 2,
                          "num_bottleneck_blocks": 6,
                          "estimate_occlusion_map": True,
                          "dense_motion_params": {
                              "block_expansion": 64,
                              "max_features": 1024,
                              "num_blocks": 5,
                              "scale_factor": 0.25,
                          },
                      },
                  },
              }
          }
      self.image_size = image_size
      if weight_path is None:
          if mobile_net:
              vox_cpk_weight_url = "https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-mobile.pdparams"
  
          else:
              if self.image_size == 512:
                  vox_cpk_weight_url = "https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-cpk-512.pdparams"
              else:
                  vox_cpk_weight_url = "https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-cpk.pdparams"
          weight_path = get_path_from_url(vox_cpk_weight_url)
  
      self.weight_path = weight_path
      self.relative = relative
      self.adapt_scale = adapt_scale
      self.find_best_frame = find_best_frame
      self.best_frame = best_frame
      self.face_detector = face_detector
      self.generator, self.kp_detector = self.load_checkpoints(
          self.cfg, self.weight_path
      )
      self.multi_person = multi_person
      self.face_enhancement = face_enhancement
      self.batch_size = batch_size
      if face_enhancement:
          from ppgan.faceutils.face_enhancement import FaceEnhancement
  
          self.faceenhancer = FaceEnhancement(batch_size=batch_size)
  
imports: import os
  import sys
  import math
  import pickle
  import yaml
  import imageio
  import numpy
  from tqdm import tqdm
  from scipy.spatial import ConvexHull
  import cv2
  import paddle
  from ppgan.utils.download import get_path_from_url
  from ppgan.utils.animate import normalize_kp
  from ppgan.modules.keypoint_detector import KPDetector
  from ppgan.models.generators.occlusion_aware import OcclusionAwareGenerator
  from ppgan.faceutils import face_detection
label_desc: Use image node_id 1 to create a new FirstOrderPredictor object  with example: obj = FirstOrderPredictor(weight_path, config, image_size, relative, adapt_scale, find_best_frame, best_frame, face_detector, multi_person, face_enhancement, batch_size, mobile_net)