code: def expm(A):
      """
      Compute the matrix exponential using Pade approximation.
  
      Parameters
      ----------
      A : (M,M) array_like or sparse matrix
          2D Array or Matrix (sparse or dense) to be exponentiated
  
      Returns
      -------
      expA : (M,M) ndarray
          Matrix exponential of `A`
  
      Notes
      -----
      This is algorithm (6.1) which is a simplification of algorithm (5.1).
  
      .. versionadded:: 0.12.0
  
      References
      ----------
      .. [1] Awad H. Al-Mohy and Nicholas J. Higham (2009)
             "A New Scaling and Squaring Algorithm for the Matrix Exponential."
             SIAM Journal on Matrix Analysis and Applications.
             31 (3). pp. 970-989. ISSN 1095-7162
  
      Examples
      --------
      >>> from scipy.sparse import csc_matrix
      >>> from scipy.sparse.linalg import expm
      >>> A = csc_matrix([[1, 0, 0], [0, 2, 0], [0, 0, 3]])
      >>> A.toarray()
      array([[1, 0, 0],
             [0, 2, 0],
             [0, 0, 3]], dtype=int64)
      >>> Aexp = expm(A)
      >>> Aexp
      <3x3 sparse matrix of type '<class 'numpy.float64'>'
          with 3 stored elements in Compressed Sparse Column format>
      >>> Aexp.toarray()
      array([[  2.71828183,   0.        ,   0.        ],
             [  0.        ,   7.3890561 ,   0.        ],
             [  0.        ,   0.        ,  20.08553692]])
      """
      return _expm(A, use_exact_onenorm="auto")
  
imports: import numpy
  from scipy.linalg._basic import solve, solve_triangular
  from scipy.sparse._base import issparse
  from scipy.sparse.linalg import spsolve
  from scipy.sparse._sputils import is_pydata_spmatrix, isintlike
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.sparse.linalg._interface import LinearOperator
  from scipy.sparse._construct import eye
  from ._expm_multiply import _ident_like, _exact_1_norm
label_desc: Use image node_id 7 for calling a global function with example usage: expm(A) and returns: _expm