code: def big_fun_with_parameters(x, y, p):
      """Big version of sl_fun, with two parameters.
  
      The two differential equations represented by sl_fun are broadcast to the
      number of rows of y, rotating between the parameters p[0] and p[1].
      Here are the differential equations:
  
          dy[0]/dt = y[1]
          dy[1]/dt = -p[0]**2 * y[0]
          dy[2]/dt = y[3]
          dy[3]/dt = -p[1]**2 * y[2]
          dy[4]/dt = y[5]
          dy[5]/dt = -p[0]**2 * y[4]
          dy[6]/dt = y[7]
          dy[7]/dt = -p[1]**2 * y[6]
          .
          .
          .
  
      """
      f = np.zeros_like(y)
      f[::2] = y[1::2]
      f[1::4] = -p[0] ** 2 * y[::4]
      f[3::4] = -p[1] ** 2 * y[2::4]
      return f
  
imports: import sys
  import numpy
  from numpy.testing import assert_, assert_array_equal, assert_allclose, assert_equal
  from pytest import raises
  from scipy.sparse import coo_matrix
  from scipy.special import erf
  from scipy.integrate._bvp import modify_mesh, estimate_fun_jac, estimate_bc_jac, compute_jac_indices, construct_global_jac, solve_bvp
label_desc: Use image node_id 22 for calling a global function with example usage: big_fun_with_parameters(x, y, p) and returns: f