class_docstr: Theoretical properties of an ARMA process for specified lag-polynomials.
  
  Parameters
  ----------
  ar : array_like
      Coefficient for autoregressive lag polynomial, including zero lag.
      Must be entered using the signs from the lag polynomial representation.
      See the notes for more information about the sign.
  ma : array_like
      Coefficient for moving-average lag polynomial, including zero lag.
  nobs : int, optional
      Length of simulated time series. Used, for example, if a sample is
      generated. See example.
  
  Notes
  -----
  Both the AR and MA components must include the coefficient on the
  zero-lag. In almost all cases these values should be 1. Further, due to
  using the lag-polynomial representation, the AR parameters should
  have the opposite sign of what one would write in the ARMA representation.
  See the examples below.
  
  The ARMA(p,q) process is described by
  
  .. math::
  
      y_{t}=\phi_{1}y_{t-1}+\ldots+\phi_{p}y_{t-p}+\theta_{1}\epsilon_{t-1}
             +\ldots+\theta_{q}\epsilon_{t-q}+\epsilon_{t}
  
  and the parameterization used in this function uses the lag-polynomial
  representation,
  
  .. math::
  
      \left(1-\phi_{1}L-\ldots-\phi_{p}L^{p}\right)y_{t} =
          \left(1+\theta_{1}L+\ldots+\theta_{q}L^{q}\right)\epsilon_{t}
  
  Examples
  --------
  ARMA(2,2) with AR coefficients 0.75 and -0.25, and MA coefficients 0.65 and 0.35
  
  >>> import statsmodels.api as sm
  >>> import numpy as np
  >>> np.random.seed(12345)
  >>> arparams = np.array([.75, -.25])
  >>> maparams = np.array([.65, .35])
  >>> ar = np.r_[1, -arparams] # add zero-lag and negate
  >>> ma = np.r_[1, maparams] # add zero-lag
  >>> arma_process = sm.tsa.ArmaProcess(ar, ma)
  >>> arma_process.isstationary
  True
  >>> arma_process.isinvertible
  True
  >>> arma_process.arroots
  array([1.5-1.32287566j, 1.5+1.32287566j])
  >>> y = arma_process.generate_sample(250)
  >>> model = sm.tsa.ARIMA(y, (2, 0, 2), trend='n').fit(disp=0)
  >>> model.params
  array([ 0.79044189, -0.23140636,  0.70072904,  0.40608028])
  
  The same ARMA(2,2) Using the from_coeffs class method
  
  >>> arma_process = sm.tsa.ArmaProcess.from_coeffs(arparams, maparams)
  >>> arma_process.arroots
  array([1.5-1.32287566j, 1.5+1.32287566j])
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code: def generate_sample(
      self, nsample=100, scale=1.0, distrvs=None, axis=0, burnin=0
  ):
      return arma_generate_sample(
          self.ar,
          self.ma,
          nsample,
          scale,
          distrvs,
          axis=axis,
          burnin=burnin,
      )
  
imports: from statsmodels.compat.numpy import NP_LT_2
  from statsmodels.compat.pandas import Appender
  import warnings
  import numpy
  from scipy import linalg, optimize, signal
  from statsmodels.tools.docstring import Docstring, remove_parameters
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 20 for calling the ArmaProcess obj's underlying member method code with example usage: obj.generate_sample(nsample, scale, distrvs, axis, burnin) and returns: arma_generate_sample