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 __init__(self, feat_dim: int):
      super(Discriminator, self).__init__()
      self.affine = torch.nn.Bilinear(feat_dim, feat_dim, 1)
      self.reset_parameters()
  
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 1 to create a new Discriminator object from inherited base classes: torch.nn with example: obj = Discriminator(feat_dim)