code: def forward(self, input_tensor):
      seq_length = input_tensor.shape[1]
  
      Q = self.Wq(input_tensor)
      K = self.Wk(input_tensor)
      V = self.Wv(input_tensor)
  
      Q = Q.reshape(
          input_tensor.shape[0],
          input_tensor.shape[1],
          self.n_heads,
          self.dq,
      ).transpose(1, 2)
      K = K.reshape(
          input_tensor.shape[0],
          input_tensor.shape[1],
          self.n_heads,
          self.dk,
      ).permute(0, 2, 3, 1)
      V = V.reshape(
          input_tensor.shape[0],
          input_tensor.shape[1],
          self.n_heads,
          self.dv,
      ).transpose(1, 2)
  
      attention_score = Q.matmul(K) / np.sqrt(self.per_head_dim)
  
      attention_mask = (
          torch.zeros(seq_length, seq_length)
          .masked_fill(
              torch.tril(torch.ones(seq_length, seq_length)) == 0,
              -np.inf,
          )
          .to(input_tensor.device)
      )
  
      attention_score = attention_score + attention_mask
  
      attention_score = torch.softmax(attention_score, dim=-1)
  
      multi_head_result = attention_score.matmul(V)
  
      if self.attention_aggregate == "concat":
          output = multi_head_result.transpose(1, 2).reshape(
              input_tensor.shape[0],
              seq_length,
              self.n_heads * self.per_head_dim,
          )
      elif self.attention_aggregate == "mean":
          output = multi_head_result.transpose(1, 2).mean(dim=2)
      else:
          raise ValueError(
              f"wrong value for aggregate {self.attention_aggregate}"
          )
      print(output.shape)
      return output
  
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 2 for calling the MaskedSelfAttention obj's underlying member method code with example usage: obj.forward(input_tensor) and returns: output