class_docstr: This class implements fitting Cox's time-varying proportional hazard model:
  
      .. math::  h(t|x(t)) = h_0(t)\exp((x(t)-\overline{x})'\beta)
  
  Parameters
  ----------
  alpha: float, optional (default=0.05)
     the level in the confidence intervals.
  penalizer: float, optional
      the coefficient of an L2 penalizer in the regression
  
  Attributes
  ----------
  params_ : Series
      The estimated coefficients. Changed in version 0.22.0: use to be ``.hazards_``
  hazard_ratios_ : Series
      The exp(coefficients)
  confidence_intervals_ : DataFrame
      The lower and upper confidence intervals for the hazard coefficients
  event_observed: Series
      The event_observed variable provided
  weights: Series
      The event_observed variable provided
  variance_matrix_ : DataFrame
      The variance matrix of the coefficients
  strata: list | str
      the strata provided
  standard_errors_: Series
      the standard errors of the estimates
  baseline_cumulative_hazard_: DataFrame
  baseline_survival_: DataFrame
class_docstr_tok: ["This","class","implements","fitting","Cox","'s","time-varying","proportional","hazard","model",":","..","math",":",":","h","(","t|x","(","t",")",")","=","h_0","(","t",")","\\exp","(","(","x","(","t",")","-\\overline","{","x","}",")","'\\beta",")","Parameters","--","--","--","--","--","alpha",":","float",",","optional","(","default=0.05",")","the","level","in","the","confidence","intervals",".","penalizer",":","float",",","optional","the","coefficient","of","an","L2","penalizer","in","the","regression","Attributes","--","--","--","--","--","params_",":","Series","The","estimated","coefficients",".","Changed","in","version","0.22.0",":","use","to","be","``",".hazards_","``","hazard_ratios_",":","Series","The","exp","(","coefficients",")","confidence_intervals_",":","DataFrame","The","lower","and","upper","confidence","intervals","for","the","hazard","coefficients","event_observed",":","Series","The","event_observed","variable","provided","weights",":","Series","The","event_observed","variable","provided","variance_matrix_",":","DataFrame","The","variance","matrix","of","the","coefficients","strata",":","list","|","str","the","strata","provided","standard_errors_",":","Series","the","standard","errors","of","the","estimates","baseline_cumulative_hazard_",":","DataFrame","baseline_survival_",":","DataFrame"]
code: def _compute_standard_errors(self, X, events, start, stop, weights):
      if self.robust:
          se = np.sqrt(
              self._compute_sandwich_estimator(
                  X, events, start, stop, weights
              ).diagonal()
          )
      else:
          se = np.sqrt(self.variance_matrix_.values.diagonal())
      return pd.Series(se, index=self.params_.index, name="se")
  
imports: from datetime import datetime
  import warnings
  import time
  from typing import Optional
  import numpy
  import pandas
  from scipy import stats
  from numpy.linalg import norm, inv
  from numpy import sum
  from scipy.linalg import solve, LinAlgError
  from autograd import elementwise_grad
  from autograd import numpy
  from lifelines.fitters import SemiParametricRegressionFitter
  from lifelines.fitters.mixins import ProportionalHazardMixin
  from lifelines.utils.printer import Printer
  from lifelines.statistics import _chisq_test_p_value, StatisticalResult
  from lifelines.exceptions import ConvergenceError, ConvergenceWarning
  from lifelines.utils import _get_index, _to_list, check_for_numeric_dtypes_or_raise, check_low_var, check_complete_separation_low_variance, check_for_immediate_deaths, check_for_instantaneous_events_at_time_zero, check_for_instantaneous_events_at_death_time, check_for_nonnegative_intervals, pass_for_numeric_dtypes_or_raise_array, inv_normal_cdf, normalize, StepSizer, check_nans_or_infs, string_rjustify, coalesce
  from lifelines import utils
label_desc: Use image node_id 23 for calling the CoxTimeVaryingFitter obj's underlying member method code with example usage: obj._compute_standard_errors(X, events, start, stop, weights) and returns: pd