code: def explain_message(
      self, out: Tensor, x_i: Tensor, x_j: Tensor
  ) -> Tensor:
      basis_messages = F.layer_norm(out, (out.size(-1),)).relu()
  
      if getattr(self, "message_scale", None) is not None:
          basis_messages = (
              basis_messages * self.message_scale.unsqueeze(-1)
          )
  
          if self.message_replacement is not None:
              if basis_messages.shape == self.message_replacement.shape:
                  basis_messages = (
                      basis_messages
                      + (1 - self.message_scale).unsqueeze(-1)
                      * self.message_replacement
                  )
              else:
                  basis_messages = basis_messages + (
                      (1 - self.message_scale).unsqueeze(-1)
                      * self.message_replacement.unsqueeze(0)
                  )
  
      self.latest_messages = basis_messages
      self.latest_source_embeddings = x_j
      self.latest_target_embeddings = x_i
  
      return basis_messages
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm, Linear, Parameter, ReLU
  from tqdm import tqdm
  from torch_geometric.explain import Explanation
  from torch_geometric.explain.algorithm import ExplainerAlgorithm
  from torch_geometric.explain.config import MaskType, ModelMode, ModelTaskLevel
  from torch_geometric.nn import MessagePassing
label_desc: Use image node_id 1 for calling a global function with example usage: explain_message(out, x_i, x_j) and returns: basis_messages