class_docstr: Class for estimating and conducting inference in an AFT model.
  
  Parameters
  ----------
  
  endog: nx1 array
      Response variables that are subject to random censoring
  
  exog: nxk array
      Matrix of covariates
  
  censors: nx1 array
      array with entries 0 or 1.  0 indicates a response was
      censored.
  
  Attributes
  ----------
  nobs : float
      Number of observations
  endog : ndarray
      Endog attay
  exog : ndarray
      Exogenous variable matrix
  censors
      Censors array but sets the max(endog) to uncensored
  nvar : float
      Number of exogenous variables
  uncens_nobs : float
      Number of uncensored observations
  uncens_endog : ndarray
      Uncensored response variables
  uncens_exog : ndarray
      Exogenous variables of the uncensored observations
  
  Methods
  -------
  
  params:
      Fits model parameters
  
  test_beta:
      Tests if beta = b0 for any vector b0.
  
  Notes
  -----
  
  The data is immediately sorted in order of increasing endogenous
  variables
  
  The last observation is assumed to be uncensored which makes
  estimation and inference possible.
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docstr: Indicated if an observation takes the same value as the next
  ordered observation.
  
  Parameters
  ----------
  endog : ndarray
      Models endogenous variable
  censors : ndarray
      arrat indicating a censored array
  
  Returns
  -------
  indic_ties : ndarray
      ties[i]=1 if endog[i]==endog[i+1] and
      censors[i]=censors[i+1]
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code: def _is_tied(self, endog, censors):
      """
      Indicated if an observation takes the same value as the next
      ordered observation.
  
      Parameters
      ----------
      endog : ndarray
          Models endogenous variable
      censors : ndarray
          arrat indicating a censored array
  
      Returns
      -------
      indic_ties : ndarray
          ties[i]=1 if endog[i]==endog[i+1] and
          censors[i]=censors[i+1]
      """
      nobs = int(self.nobs)
      endog_idx = endog[np.arange(nobs - 1)] == (
          endog[np.arange(nobs - 1) + 1]
      )
      censors_idx = censors[np.arange(nobs - 1)] == (
          censors[np.arange(nobs - 1) + 1]
      )
      indic_ties = endog_idx * censors_idx  # Both true
      return np.int_(indic_ties)
  
imports: import warnings
  import numpy
  from scipy import optimize
  from scipy.stats import chi2
  from statsmodels.regression.linear_model import OLS, WLS
  from statsmodels.tools import add_constant
  from statsmodels.tools.sm_exceptions import IterationLimitWarning
  from .descriptive import _OptFuncts
label_desc: Use image node_id 2 for calling the emplikeAFT obj's underlying member method code with example usage: obj._is_tied(endog, censors) and returns: np