class_docstr: Base class for all graph blocks, implementing all methods needed for processing the adjacency matrix
docstr: Transform the adjacency matrix needed for the computation.
  
  Since repeated application of forward computation on the standard adjacency matrix can lead to numerical
  instabilities and exploding/vanishing gradients when used in a deep neural network model. To alleviate this
  problem, the following renormalization trick is used:
  
  A^bar := A + I_n
  
  :param adj: The standard adjacency matrix.
  :return: The processed adjacency matrix A^hat.
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code: def preprocess_adj_to_adj_bar(
      cls, adj: torch.Tensor
  ) -> Union[torch.Tensor, np.ndarray]:
      """Transform the adjacency matrix needed for the computation.
  
      Since repeated application of forward computation on the standard adjacency matrix can lead to numerical
      instabilities and exploding/vanishing gradients when used in a deep neural network model. To alleviate this
      problem, the following renormalization trick is used:
  
      A^bar := A + I_n
  
      :param adj: The standard adjacency matrix.
      :return: The processed adjacency matrix A^hat.
      """
  
      batch_dim = 0
      if len(adj.shape) > 2:
          # If a batch of adjacency matrices is given compute only once and repeat
          batch_dim = adj.shape[0]
      else:
          adj = adj.unsqueeze(0)
      # Our framework currently does not naturally support edge features  and  is  limited  to  undirected  graphs  (
      # weighted  or  unweighted). Results  on  NELL  however show that it is possible to handle both directed edges
      # and edge features by representing the original directed graph as an undirected bipartite graph with additional
      # nodes that represent edges in the original graph (see Section 5.1 for details).
      # - https://arxiv.org/abs/1609.02907 THUS --> adjacency matrix has to be symmetric (-> thus square)
      assert (
          adj.shape[-1] == adj.shape[-2]
      ), "The adj matrix should be a square matrix"
  
      # Add the feature of the node itself. For example, the first row of the result matrix should contain features of
      # node A too.
      adj_bar = adj + torch.eye(
          adj.shape[-2], device=adj.device
      ).repeat(adj.shape[0], 1, 1)
  
      if batch_dim == 0:
          adj_bar = adj_bar.squeeze(0)
  
      return adj_bar
  
imports: from collections import OrderedDict
  from typing import Union, List, Sequence, Dict, Optional, Callable
  import numpy
  import torch
  from torch import nn
  from torch.nn.parameter import Parameter
  from maze.core.annotations import override
  from maze.core.utils.factory import Factory
  from maze.perception.blocks.shape_normalization import ShapeNormalizationBlock
label_desc: Use image node_id 2 for calling the GraphAdjacencyMethods obj's underlying member method code with example usage: obj.preprocess_adj_to_adj_bar(cls, adj) and returns: adj_bar