class_docstr: Construct two-layer MLP-type aggreator for GIN model
code: def __init__(self, input_dim, hidden_dim, output_dim):
      super().__init__()
      self.linears = nn.ModuleList()
      # two-layer MLP
      self.linears.append(nn.Linear(input_dim, hidden_dim, bias=False))
      self.linears.append(nn.Linear(hidden_dim, output_dim, bias=False))
      self.batch_norm = nn.BatchNorm1d((hidden_dim))
  
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 1 to create a new MLP object from inherited base classes: nn with example: obj = MLP(input_dim, hidden_dim, output_dim)