class_docstr: The problem of fitting the sum of exponentials with linear and
  quadratic degrees to data, [1]_.
  
  Number of variables --- 11, number of residuals --- 65, no bounds.
  
  .. [1] Brett M. Averick et al. "The MINPACK-2 Test Problem Collection",
         p. 27
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code: def jac(self, x):
      J = np.empty((self.m, self.n))
      e0 = np.exp(-x[4] * self.t)
      e1 = np.exp(-x[5] * (self.t - x[8]) ** 2)
      e2 = np.exp(-x[6] * (self.t - x[9]) ** 2)
      e3 = np.exp(-x[7] * (self.t - x[10]) ** 2)
      J[:, 0] = e0
      J[:, 1] = e1
      J[:, 2] = e2
      J[:, 3] = e3
      J[:, 4] = -x[0] * self.t * e0
      J[:, 5] = -x[1] * (self.t - x[8]) ** 2 * e1
      J[:, 6] = -x[2] * (self.t - x[9]) ** 2 * e2
      J[:, 7] = -x[3] * (self.t - x[10]) ** 2 * e3
      J[:, 8] = 2 * x[1] * x[5] * (self.t - x[8]) * e1
      J[:, 9] = 2 * x[2] * x[6] * (self.t - x[9]) * e2
      J[:, 10] = 2 * x[3] * x[7] * (self.t - x[10]) * e3
      return J
  
imports: import inspect
  import sys
  import numpy
  from numpy.polynomial.chebyshev import Chebyshev
  from scipy.integrate import odeint
label_desc: Use image node_id 3 for calling the GaussianFitting obj's underlying member method code with example usage: obj.jac(x) and returns: J