docstr: emb_dim (int): node embedding dimensionality
docstr_tok: ["emb_dim","(","int",")",":","node","embedding","dimensionality"]
code: def __init__(self, emb_dim):
      """
      emb_dim (int): node embedding dimensionality
      """
  
      super(GINConv, self).__init__()
  
      self.mlp = nn.Sequential(
          nn.Linear(emb_dim, emb_dim),
          nn.BatchNorm1d(emb_dim),
          nn.ReLU(),
          nn.Linear(emb_dim, emb_dim),
      )
      self.eps = nn.Parameter(torch.Tensor([0]))
  
      self.bond_encoder = BondEncoder(emb_dim=emb_dim)
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import SumPooling
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
label_desc: Use image node_id 1 to create a new GINConv object from inherited base classes: nn with example: obj = GINConv(emb_dim)