code: def _condition_3_13(A_1_norm, n0, m_max, ell):
      """
      A helper function for the _expm_multiply_* functions.
  
      Parameters
      ----------
      A_1_norm : float
          The precomputed 1-norm of A.
      n0 : int
          Number of columns in the _expm_multiply_* B matrix.
      m_max : int
          A value related to a bound.
      ell : int
          The number of columns used in the 1-norm approximation.
          This is usually taken to be small, maybe between 1 and 5.
  
      Returns
      -------
      value : bool
          Indicates whether or not the condition has been met.
  
      Notes
      -----
      This is condition (3.13) in Al-Mohy and Higham (2011).
  
      """
  
      # This is the rhs of equation (3.12).
      p_max = _compute_p_max(m_max)
      a = 2 * ell * p_max * (p_max + 3)
  
      # Evaluate the condition (3.13).
      b = _theta[m_max] / float(n0 * m_max)
      return A_1_norm <= a * b
  
imports: import numpy
  import scipy.linalg
  import scipy.sparse.linalg
  from scipy.sparse.linalg import aslinearoperator
  from scipy.sparse.sputils import is_pydata_spmatrix
label_desc: Use image node_id 12 for calling a global function with example usage: _condition_3_13(A_1_norm, n0, m_max, ell) and returns: unknown