class_docstr: Description
  -----------
  A discriminator used to let the network to discrimate
  between positive (neighborhood of center node) and
  negative (any neighborhood in graph) samplings.
  
  Parameters
  ----------
  feat_dim : int
      The number of channels of node features.
class_docstr_tok: ["Description","--","--","--","--","--","-","A","discriminator","used","to","let","the","network","to","discrimate","between","positive","(","neighborhood","of","center","node",")","and","negative","(","any","neighborhood","in","graph",")","samplings",".","Parameters","--","--","--","--","--","feat_dim",":","int","The","number","of","channels","of","node","features","."]
code: def reset_parameters(self):
      torch.nn.init.xavier_uniform_(self.affine.weight)
      torch.nn.init.zeros_(self.affine.bias)
  
imports: from typing import Optional
  import dgl
  import torch
  import torch.nn
  from dgl import DGLGraph
  from dgl.nn import GraphConv
  from torch import Tensor
label_desc: Use image node_id 2 for calling the Discriminator obj's underlying member method code with example usage: obj.reset_parameters() without return types