class_docstr: A GNN prediction head for graph-level prediction tasks.
  A post message passing layer (as specified by :obj:`cfg.gnn.post_mp`) is
  used to transform the pooled graph-level embeddings using an MLP.
  
  Args:
      dim_in (int): The input feature dimension.
      dim_out (int): The output feature dimension.
class_docstr_tok: ["A","GNN","prediction","head","for","graph-level","prediction","tasks",".","A","post","message","passing","layer","(","as","specified","by",":","obj",":","`","cfg.gnn.post_mp","`",")","is","used","to","transform","the","pooled","graph-level","embeddings","using","an","MLP",".","Args",":","dim_in","(","int",")",":","The","input","feature","dimension",".","dim_out","(","int",")",":","The","output","feature","dimension","."]
code: def _apply_index(self, batch):
      return batch.graph_feature, batch.y
  
imports: import torch
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.config import cfg
  from torch_geometric.graphgym.models.layer import MLP, new_layer_config
  from torch_geometric.graphgym.register import register_head
label_desc: Use image node_id 2 for calling the GNNGraphHead obj's underlying member method code with example usage: obj._apply_index(batch) and returns: batch,  batch