code: def __init__(self, config, embedding_lr=1.0):
      self.config = config
      self.neg_num = self.config.neg_num
      self.num_nodes = self.config.num_nodes
      self.embed_dim = self.config.embed_dim
      self.is_sparse = self.config.is_sparse
      self.is_distributed = self.config.is_distributed
      self.embedding_lr = embedding_lr
  
      self.pyreader = L.py_reader(
          capacity=70,
          shapes=[[-1, 1, 1], [-1, self.neg_num + 1, 1]],
          dtypes=["int64", "int64"],
          lod_levels=[0, 0],
          name="train",
          use_double_buffer=True,
      )
  
      bound = 1.0 / math.sqrt(self.embed_dim)
      self.embed_init = F.initializer.Uniform(low=-bound, high=bound)
      self.loss = None
      max_hidden_size = int(math.pow(2, 31) / 4 / self.num_nodes)
      self.num_part = int(
          math.ceil(1.0 * self.embed_dim / max_hidden_size)
      )
  
file_path: PGL/legacy/examples/distribute_metapath2vec/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 Metapath2vecModel object from inherited base classes: object with example: obj = Metapath2vecModel(config, embedding_lr)