code: def __init__(
      self,
      k,
      feature_dims,
      emb_dims,
      output_classes,
      input_dims=3,
      dropout_prob=0.5,
  ):
      super(Model, self).__init__()
  
      self.nng = KNNGraph(k)
      self.conv = nn.ModuleList()
  
      self.num_layers = len(feature_dims)
      for i in range(self.num_layers):
          self.conv.append(
              EdgeConv(
                  feature_dims[i - 1] if i > 0 else input_dims,
                  feature_dims[i],
                  batch_norm=True,
              )
          )
  
      self.proj = nn.Linear(sum(feature_dims), emb_dims[0])
  
      self.embs = nn.ModuleList()
      self.bn_embs = nn.ModuleList()
      self.dropouts = nn.ModuleList()
  
      self.num_embs = len(emb_dims) - 1
      for i in range(1, self.num_embs + 1):
          self.embs.append(
              nn.Linear(
                  # * 2 because of concatenation of max- and mean-pooling
                  emb_dims[i - 1] if i > 1 else (emb_dims[i - 1] * 2),
                  emb_dims[i],
              )
          )
          self.bn_embs.append(nn.BatchNorm1d(emb_dims[i]))
          self.dropouts.append(nn.Dropout(dropout_prob))
  
      self.proj_output = nn.Linear(emb_dims[-1], output_classes)
  
file_path: dgl/examples/pytorch/pointcloud/edgeconv/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import EdgeConv, KNNGraph
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(k, feature_dims, emb_dims, output_classes, input_dims, dropout_prob)