code: def __init__(
      self,
      output_path="output_dir",
      weight_path=None,
      model_type=None,
      seed=None,
      size=1024,
      style_dim=512,
      n_mlp=8,
      channel_multiplier=2,
  ):
      self.output_path = output_path
  
      if weight_path is None:
          if model_type in model_cfgs.keys():
              weight_path = get_path_from_url(
                  model_cfgs[model_type]["model_urls"]
              )
              size = model_cfgs[model_type].get("size", size)
              style_dim = model_cfgs[model_type].get(
                  "style_dim", style_dim
              )
              n_mlp = model_cfgs[model_type].get("n_mlp", n_mlp)
              channel_multiplier = model_cfgs[model_type].get(
                  "channel_multiplier", channel_multiplier
              )
              checkpoint = paddle.load(weight_path)
          else:
              raise ValueError(
                  "Predictor need a weight path or a pretrained model type"
              )
      else:
          checkpoint = paddle.load(weight_path)
  
      self.generator = StyleGANv2Generator(
          size, style_dim, n_mlp, channel_multiplier
      )
      self.generator.set_state_dict(checkpoint)
      self.generator.eval()
  
      if seed is not None:
          paddle.seed(seed)
          random.seed(seed)
          np.random.seed(seed)
  
file_path: PaddleHub/modules/image/Image_gan/gan/styleganv2_editing/basemodel.py
imports: import os
  import random
  import numpy
  import paddle
  from ppgan.models.generators import StyleGANv2Generator
  from ppgan.utils.download import get_path_from_url
  from ppgan.utils.visual import make_grid, tensor2img, save_image
label_desc: Use image node_id 1 to create a new StyleGANv2Predictor object  with example: obj = StyleGANv2Predictor(output_path, weight_path, model_type, seed, size, style_dim, n_mlp, channel_multiplier)