code: def __init__(self, config, mode="gpu"):
      super(WalkBasedModel, self).__init__()
  
      self.config = config
      self.mode = mode
      self.embed_size = self.config.embed_size
      self.hidden_size = self.config.hidden_size
      self.neg_num = self.config.neg_num
  
      embed_init = nn.initializer.Uniform(
          low=-1.0 / math.sqrt(self.embed_size),
          high=1.0 / math.sqrt(self.embed_size),
      )
      emb_attr = paddle.ParamAttr(
          name="node_embedding", initializer=embed_init
      )
  
      self.embedding = getattr(
          E, self.config.embed_type, "BaseEmbedding"
      )(
          self.config.num_nodes,
          self.embed_size,
          sparse=self.config.lazy_mode,
          weight_attr=emb_attr,
      )
  
      if len(self.config.slots) > 0:
          self.slot_embed_dict = OrderedDict()
          for slot in self.config.slots:
              self.slot_embed_dict[slot] = getattr(
                  E, self.config.embed_type, "BaseEmbedding"
              )(
                  self.config.num_nodes,
                  self.embed_size,
                  sparse=self.config.lazy_mode,
                  weight_attr=paddle.ParamAttr(
                      name="slot_%s_embedding" % slot
                  ),
              )
          if self.mode == "gpu":
              self.slot_embed_dict = nn.LayerDict(
                  sublayers=self.slot_embed_dict
              )
  
      self.loss_fn = paddle.nn.BCEWithLogitsLoss()
  
imports: import math
  import time
  from collections import OrderedDict
  import paddle
  import paddle.nn
  import paddle.distributed.fleet
  import pgl
  import pgl.nn
  from pgl.distributed import helper
  from collections import OrderedDict
  import models.embedding
  import models.layers
label_desc: Use image node_id 1 to create a new WalkBasedModel object from inherited base classes: nn with example: obj = WalkBasedModel(config, mode)