code: def __init__(
      self,
      in_channels,
      num_classes,
      hidden_channels,
      num_layers,
      heads,
      dropout=0.3,
  ):
      super().__init__()
  
      self.label_emb = MaskLabel(num_classes, in_channels)
  
      self.convs = torch.nn.ModuleList()
      self.norms = torch.nn.ModuleList()
      for i in range(1, num_layers + 1):
          if i < num_layers:
              out_channels = hidden_channels // heads
              concat = True
          else:
              out_channels = num_classes
              concat = False
          conv = TransformerConv(
              in_channels,
              out_channels,
              heads,
              concat=concat,
              beta=True,
              dropout=dropout,
          )
          self.convs.append(conv)
          in_channels = hidden_channels
  
          if i < num_layers:
              self.norms.append(torch.nn.LayerNorm(hidden_channels))
  
file_path: pytorch_geometric/examples/unimp_arxiv.py
imports: import os.path
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import PygNodePropPredDataset
  import torch_geometric.transforms
  from torch_geometric.nn import MaskLabel, TransformerConv
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 1 to create a new UniMP object from inherited base classes: torch.nn with example: obj = UniMP(in_channels, num_classes, hidden_channels, num_layers, heads, dropout)