code: def __init__(
      self,
      channels: int,
      pe_dim: int,
      num_layers: int,
      attn_type: str,
      attn_kwargs: Dict[str, Any],
  ):
      super().__init__()
  
      self.node_emb = Embedding(28, channels - pe_dim)
      self.pe_lin = Linear(20, pe_dim)
      self.pe_norm = BatchNorm1d(20)
      self.edge_emb = Embedding(4, channels)
  
      self.convs = ModuleList()
      for _ in range(num_layers):
          nn = Sequential(
              Linear(channels, channels),
              ReLU(),
              Linear(channels, channels),
          )
          conv = GPSConv(
              channels,
              GINEConv(nn),
              heads=4,
              attn_type=attn_type,
              attn_kwargs=attn_kwargs,
          )
          self.convs.append(conv)
  
      self.mlp = Sequential(
          Linear(channels, channels // 2),
          ReLU(),
          Linear(channels // 2, channels // 4),
          ReLU(),
          Linear(channels // 4, 1),
      )
      self.redraw_projection = RedrawProjection(
          self.convs,
          redraw_interval=1000 if attn_type == "performer" else None,
      )
  
file_path: pytorch_geometric/examples/graph_gps.py
imports: import argparse
  import os.path
  from typing import Any, Dict, Optional
  import torch
  from torch.nn import BatchNorm1d, Embedding, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  import torch_geometric.transforms
  from torch_geometric.datasets import ZINC
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINEConv, GPSConv, global_add_pool
  from torch_geometric.nn.attention import PerformerAttention
label_desc: Use image node_id 1 to create a new GPS object from inherited base classes: torch.nn with example: obj = GPS(channels, pe_dim, num_layers, attn_type, attn_kwargs)