code: def __init__(
      self, input_dim, output_dim, n_heads, attention_aggregate="mean"
  ):
      super(MaskedSelfAttention, self).__init__()
  
      self.attention_aggregate = attention_aggregate
      self.input_dim = input_dim
      self.output_dim = output_dim
  
      self.n_heads = n_heads
  
      if attention_aggregate == "concat":
          self.per_head_dim = self.dq = self.dk = self.dv = (
              output_dim // n_heads
          )
      elif attention_aggregate == "mean":
          self.per_head_dim = self.dq = self.dk = self.dv = output_dim
      else:
          raise ValueError(
              f"wrong value for aggregate {attention_aggregate}"
          )
  
      self.Wq = nn.Linear(input_dim, n_heads * self.dq, bias=False)
      self.Wk = nn.Linear(input_dim, n_heads * self.dk, bias=False)
      self.Wv = nn.Linear(input_dim, n_heads * self.dv, bias=False)
  
imports: import torch
  import numpy
  import torch.nn
  from typing import List
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 1 to create a new MaskedSelfAttention object from inherited base classes: nn with example: obj = MaskedSelfAttention(input_dim, output_dim, n_heads, attention_aggregate)