class_docstr: MLP used for predictor. The MLP has one hidden layer.
  Args:
      input_size (int): Size of input features.
      output_size (int): Size of output features.
      hidden_size (int, optional): Size of hidden layer. (default: :obj:`4096`).
class_docstr_tok: ["MLP","used","for","predictor",".","The","MLP","has","one","hidden","layer",".","Args",":","input_size","(","int",")",":","Size","of","input","features",".","output_size","(","int",")",":","Size","of","output","features",".","hidden_size","(","int",",","optional",")",":","Size","of","hidden","layer",".","(","default",":",":","obj",":","`","4096","`",")","."]
code: def forward(self, x):
      return self.net(x)
  
imports: import copy
  import dgl
  import torch
  from dgl.nn.pytorch.conv import GraphConv, SAGEConv
  from torch import nn
  from torch.nn import BatchNorm1d, Parameter
  from torch.nn.init import ones_, zeros_
label_desc: Use image node_id 2 for calling the MLP_Predictor obj's underlying member method code with example usage: obj.forward(x) and returns: self