code: def gcn_filter(A, symmetric=True):
      r"""
      Computes the graph filter described in
      [Kipf & Welling (2017)](https://arxiv.org/abs/1609.02907).
      :param A: array or sparse matrix with rank 2 or 3;
      :param symmetric: boolean, whether to normalize the matrix as
      \(\D^{-\frac{1}{2}}\A\D^{-\frac{1}{2}}\) or as \(\D^{-1}\A\);
      :return: array or sparse matrix with rank 2 or 3, same as A;
      """
      out = copy.deepcopy(A)
      if isinstance(A, list) or (
          isinstance(A, np.ndarray) and A.ndim == 3
      ):
          for i in range(len(A)):
              out[i] = A[i]
              out[i][np.diag_indices_from(out[i])] += 1
              out[i] = normalized_adjacency(out[i], symmetric=symmetric)
      else:
          if hasattr(out, "tocsr"):
              out = out.tocsr()
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              out[np.diag_indices_from(out)] += 1
          out = normalized_adjacency(out, symmetric=symmetric)
  
      if sp.issparse(out):
          out.sort_indices()
      return out
  
imports: import copy
  import warnings
  from functools import partial
  import numpy
  import tensorflow
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse.linalg import ArpackNoConvergence
label_desc: Use image node_id 7 for calling a global function with example usage: gcn_filter(A, symmetric) and returns: out