code: def run(self, latent1, latent2, weights=[0.5] * 18):
      latent1 = paddle.to_tensor(latent1).unsqueeze(0)
      latent2 = paddle.to_tensor(latent2).unsqueeze(0)
      assert (
          latent1.shape[1] == latent2.shape[1] == len(weights)
      ), "latents and their weights should have the same level nums."
      mix_latent = []
      for i, weight in enumerate(weights):
          mix_latent.append(
              latent1[:, i : i + 1] * weight
              + latent2[:, i : i + 1] * (1 - weight)
          )
      mix_latent = paddle.concat(mix_latent, 1)
      latent_n = paddle.concat([latent1, latent2, mix_latent], 0)
      generator = self.generator
      img_gen, _ = generator(
          [latent_n], input_is_latent=True, randomize_noise=False
      )
      imgs = make_image(img_gen)
      src_img1 = imgs[0]
      src_img2 = imgs[1]
      dst_img = imgs[2]
  
      return src_img1, src_img2, dst_img
  
file_path: PaddleHub/modules/image/Image_gan/gan/styleganv2_mixing/model.py
imports: import os
  import cv2
  import numpy
  import paddle
  from .basemodel import StyleGANv2Predictor
label_desc: Use image node_id 1 for calling the StyleGANv2MixingPredictor obj's underlying member method code with example usage: obj.run(latent1, latent2, weights) and returns: src_img1,  src_img2,  dst_img