code: def simulate(self, m, s, lambd, p, e1, e2, ts, nrepl):
      T = ts[-1]
      # simulate number of jumps
      N = np.random.poisson(lambd * T, size=(nrepl, 1))
  
      jumps = []
      nobs = len(ts)
      jumps = np.zeros((nrepl, nobs))
      for j in range(nrepl):
          # simulate jump arrival time
          t = T * np.random.rand(N[j])
          t = np.sort(t)
  
          # simulate jump size
          ww = np.random.binomial(1, p, size=(N[j]))
          S = ww * np.random.exponential(e1, size=(N[j])) - (
              1 - ww
          ) * np.random.exponential(e2, N[j])
  
          # put things together
          CumS = np.cumsum(S)
          jumps_ts = np.zeros(nobs)
          for n in range(nobs):
              Events = sum(t <= ts[n]) - 1
              jumps_ts[n] = 0
              if Events:
                  jumps_ts[n] = CumS[Events]
  
          jumps[j, :] = jumps_ts
  
      D_Diff = np.zeros((nrepl, nobs))
      for k in range(nobs):
          Dt = ts[k]
          if k > 1:
              Dt = ts[k] - ts[k - 1]
  
          D_Diff[:, k] = m * Dt + s * np.sqrt(Dt) * np.random.normal(
              size=nrepl
          )
  
      x = np.hstack(
          (np.zeros((nrepl, 1)), np.cumsum(D_Diff, 1) + jumps)
      )
      return x
  
imports: import numpy
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling the JumpDiffusionKou obj's underlying member method code with example usage: obj.simulate(m, s, lambd, p, e1, e2, ts, nrepl) and returns: x