class_docstr: Construct two-layer MLP-type aggreator for GIN model
code: def forward(self, x):
      h = x
      h = F.relu(self.batch_norm(self.linears[0](h)))
      return self.linears[1](h)
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import GINDataset
  from dgl.dataloading import GraphDataLoader
  from dgl.nn.pytorch.conv import GINConv
  from dgl.nn.pytorch.glob import SumPooling
  from sklearn.model_selection import StratifiedKFold
  from torch.utils.data.sampler import SubsetRandomSampler
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: self