code: def __init__(
      self,
      g,
      in_feats,
      hiddens,
      n_classes,
      activation,
      feat_drop,
      edge_drop,
      alpha,
      k,
  ):
      super(APPNP, self).__init__()
      self.g = g
  
      with self.name_scope():
          self.layers = nn.Sequential()
          # input layer
          self.layers.add(nn.Dense(hiddens[0], in_units=in_feats))
          # hidden layers
          for i in range(1, len(hiddens)):
              self.layers.add(
                  nn.Dense(hiddens[i], in_units=hiddens[i - 1])
              )
          # output layer
          self.layers.add(nn.Dense(n_classes, in_units=hiddens[-1]))
          self.activation = activation
          if feat_drop:
              self.feat_drop = nn.Dropout(feat_drop)
          else:
              self.feat_drop = lambda x: x
          self.propagate = APPNPConv(k, alpha, edge_drop)
  
file_path: dgl/examples/mxnet/appnp/appnp.py
imports: import argparse
  import time
  import dgl
  import mxnet
  import numpy
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset, register_data_args
  from dgl.nn.mxnet.conv import APPNPConv
  from mxnet import gluon, nd
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new APPNP object from inherited base classes: nn with example: obj = APPNP(g, in_feats, hiddens, n_classes, activation, feat_drop, edge_drop, alpha, k)