code: def __init__(
      self,
      node_type,
      edge_type,
      max_id,
      dim,
      walk_len=3,
      walk_p=1,
      walk_q=1,
      left_win_size=1,
      right_win_size=1,
      num_negs=5,
      feature_idx=-1,
      feature_dim=0,
      use_id=True,
      embedding_dim=16,
      metric="mrr",
      combiner="add",
      neg_condition="",
      *args,
      **kwargs,
  ):
      super(DeepWalk, self).__init__(
          node_type,
          edge_type,
          max_id,
          walk_len=walk_len,
          walk_p=walk_p,
          walk_q=walk_q,
          left_win_size=left_win_size,
          right_win_size=right_win_size,
          num_negs=num_negs,
          metric=metric,
          neg_condition=neg_condition,
          *args,
          **kwargs,
      )
  
      self.dim = dim
      self._target_encoder = tf_euler.utils.encoders.ShallowEncoder(
          dim=dim,
          feature_idx=feature_idx,
          feature_dim=feature_dim,
          max_id=max_id if use_id else -1,
          embedding_dim=embedding_dim,
          combiner=combiner,
      )
      self._context_encoder = tf_euler.utils.encoders.ShallowEncoder(
          dim=dim,
          feature_idx=feature_idx,
          feature_dim=feature_dim,
          max_id=max_id if use_id else -1,
          embedding_dim=embedding_dim,
          combiner=combiner,
      )
  
file_path: euler/examples/deepwalk/deepwalk.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tf_euler
  from tf_euler.python.mp_utils.base_gnn import BaseGNNNet
  from tf_euler.python.mp_utils.base import SuperviseModel, UnsuperviseModel
label_desc: Use image node_id 1 to create a new DeepWalk object from inherited base classes: BaseNode2Vec with example: obj = DeepWalk(node_type, edge_type, max_id, dim, walk_len, walk_p, walk_q, left_win_size, right_win_size, num_negs, feature_idx, feature_dim, use_id, embedding_dim, metric, combiner, neg_condition)