code: def __init__(
      self,
      in_size,
      out_size,
      hidden_size=64,
      dropout=0.1,
      num_hops=10,
      alpha=0.1,
  ):
      super().__init__()
  
      self.f_theta = nn.Sequential(
          nn.Dropout(dropout),
          nn.Linear(in_size, hidden_size),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(hidden_size, out_size),
      )
      self.num_hops = num_hops
      self.A_dropout = nn.Dropout(dropout)
      self.alpha = alpha
  
file_path: dgl/examples/sparse/appnp.py
imports: import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data import CoraGraphDataset
  from torch.optim import Adam
label_desc: Use image node_id 1 to create a new APPNP object from inherited base classes: nn with example: obj = APPNP(in_size, out_size, hidden_size, dropout, num_hops, alpha)