code: def arma_loglike(
      endog, ar_params=None, ma_params=None, sigma2=1, prefix=None
  ):
      """
      Compute the log-likelihood of the given data assuming an ARMA process.
  
      Parameters
      ----------
      endog : ndarray
          The observed time-series process.
      ar_params : ndarray, optional
          Autoregressive parameters.
      ma_params : ndarray, optional
          Moving average parameters.
      sigma2 : ndarray, optional
          The ARMA innovation variance. Default is 1.
      prefix : str, optional
          The BLAS prefix associated with the datatype. Default is to find the
          best datatype based on given input. This argument is typically only
          used internally.
  
      Returns
      -------
      float
          The joint loglikelihood.
      """
      llf_obs = arma_loglikeobs(
          endog,
          ar_params=ar_params,
          ma_params=ma_params,
          sigma2=sigma2,
          prefix=prefix,
      )
      return np.sum(llf_obs)
  
file_path: statsmodels/statsmodels/tsa/innovations/arma_innovations.py
imports: import numpy
  from statsmodels.tsa import arima_process
  from statsmodels.tsa.statespace.tools import prefix_dtype_map
  from statsmodels.tools.numdiff import _get_epsilon, approx_fprime_cs
  from scipy.linalg.blas import find_best_blas_type
  from .None import _arma_innovations
label_desc: Use image node_id 2 for calling a global function with example usage: arma_loglike(endog, ar_params, ma_params, sigma2, prefix) and returns: np