code: def _sinch(x):
      """
      Stably evaluate sinch.
  
      Notes
      -----
      The strategy of falling back to a sixth order Taylor expansion
      was suggested by the Spallation Neutron Source docs
      which was found on the internet by google search.
      http://www.ornl.gov/~t6p/resources/xal/javadoc/gov/sns/tools/math/ElementaryFunction.html
      The details of the cutoff point and the Horner-like evaluation
      was picked without reference to anything in particular.
  
      Note that sinch is not currently implemented in scipy.special,
      whereas the "engineer's" definition of sinc is implemented.
      The implementation of sinc involves a scaling factor of pi
      that distinguishes it from the "mathematician's" version of sinc.
  
      """
  
      # If x is small then use sixth order Taylor expansion.
      # How small is small? I am using the point where the relative error
      # of the approximation is less than 1e-14.
      # If x is large then directly evaluate sinh(x) / x.
      x2 = x * x
      if abs(x) < 0.0135:
          return 1 + (x2 / 6.0) * (1 + (x2 / 20.0) * (1 + (x2 / 42.0)))
      else:
          return np.sinh(x) / x
  
imports: from __future__ import division, print_function, absolute_import
  import math
  import numpy
  import scipy.special
  from scipy.linalg.basic import solve, solve_triangular
  from scipy.sparse.base import isspmatrix
  from scipy.sparse.construct import eye
  from scipy.sparse.linalg import spsolve
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.sparse.linalg.interface import LinearOperator
label_desc: Use image node_id 12 for calling a global function with example usage: _sinch(x) and returns: unknown, unknown