code: def _expm_multiply_interval_core_1(A, X, h, mu, m_star, s, q, tol):
      """
      A helper function, for the case q > s and q % s == 0.
      """
      d = q // s
      input_shape = X.shape[1:]
      K_shape = (m_star + 1,) + input_shape
      K = np.empty(K_shape, dtype=X.dtype)
      for i in range(s):
          Z = X[i * d]
          K[0] = Z
          high_p = 0
          for k in range(1, d + 1):
              F = K[0]
              c1 = _exact_inf_norm(F)
              for p in range(1, m_star + 1):
                  if p > high_p:
                      K[p] = h * A.dot(K[p - 1]) / float(p)
                  coeff = float(pow(k, p))
                  F = F + coeff * K[p]
                  inf_norm_K_p_1 = _exact_inf_norm(K[p])
                  c2 = coeff * inf_norm_K_p_1
                  if c1 + c2 <= tol * _exact_inf_norm(F):
                      break
                  c1 = c2
              X[k + i * d] = np.exp(k * h * mu) * F
      return X, 1
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import scipy.linalg
  import scipy.sparse.linalg
  from scipy.sparse.linalg import aslinearoperator
label_desc: Use image node_id 15 for calling a global function with example usage: _expm_multiply_interval_core_1(A, X, h, mu, m_star, s, q, tol) and returns: X,  int