class_docstr: A general Graph Neural Network (GNN) model.
  
  The GNN model consists of three main components:
  
  1. An encoder to transform input features into a fixed-size embedding
     space.
  2. A processing or message passing stage for information exchange between
     nodes.
  3. A head to produce the final output features/predictions.
  
  The configuration of each component is determined by the underlying
  configuration in :obj:`cfg`.
  
  Args:
      dim_in (int): The input feature dimension.
      dim_out (int): The output feature dimension.
      **kwargs (optional): Additional keyword arguments.
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code: def forward(self, batch):
      for module in self.children():
          batch = module(batch)
      return batch
  
imports: import torch
  import torch.nn.functional
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.config import cfg
  from torch_geometric.graphgym.init import init_weights
  from torch_geometric.graphgym.models.layer import BatchNorm1dNode, GeneralLayer, GeneralMultiLayer, new_layer_config
  from torch_geometric.graphgym.register import register_stage
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(batch) and returns: batch