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 __init__(self, input_size, output_size, hidden_size=512):
      super().__init__()
  
      self.net = nn.Sequential(
          nn.Linear(input_size, hidden_size, bias=True),
          nn.PReLU(1),
          nn.Linear(hidden_size, output_size, bias=True),
      )
      self.reset_parameters()
  
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 1 to create a new MLP_Predictor object from inherited base classes: nn with example: obj = MLP_Predictor(input_size, output_size, hidden_size)