code: def __init__(
      self,
      num_nodes,
      hidden_size=16,
      neg_num=5,
      is_sparse=False,
      is_distributed=False,
      embedding_lr=1.0,
  ):
      self.pyreader = L.py_reader(
          capacity=70,
          shapes=[[-1, 1, 1], [-1, neg_num + 1, 1]],
          dtypes=["int64", "int64"],
          lod_levels=[0, 0],
          name="train",
          use_double_buffer=True,
      )
  
      self.num_nodes = num_nodes
      self.neg_num = neg_num
  
      self.embed_init = F.initializer.Uniform(
          low=-1.0 / math.sqrt(hidden_size),
          high=1.0 / math.sqrt(hidden_size),
      )
      self.is_sparse = is_sparse
      self.is_distributed = is_distributed
      self.hidden_size = hidden_size
      self.loss = None
      self.embedding_lr = embedding_lr
      max_hidden_size = int(math.pow(2, 31) / 4 / num_nodes)
      self.num_part = int(
          math.ceil(1.0 * hidden_size / max_hidden_size)
      )
  
file_path: PGL/legacy/examples/distribute_deepwalk/model.py
imports: from __future__ import division
  from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import unicode_literals
  import math
  import paddle.fluid.layers
  import paddle.fluid
label_desc: Use image node_id 1 to create a new DeepwalkModel object from inherited base classes: object with example: obj = DeepwalkModel(num_nodes, hidden_size, neg_num, is_sparse, is_distributed, embedding_lr)