code: def test_survival_difference_at_fixed_point_in_time_test_left_censoring():
      T1 = np.random.exponential(1e-6, size=1000)
      T2 = np.random.exponential(1e-6, size=1000)
      E = T1 > T2
      T = np.maximum(T1, T2)
      kmf1 = KaplanMeierFitter().fit_left_censoring(T)
      kmf2 = KaplanMeierFitter().fit_left_censoring(2 * T)
      result = stats.survival_difference_at_fixed_point_in_time_test(
          T.mean(), kmf1, kmf2
      )
      assert result.p_value < 0.05
  
imports: import numpy
  import pandas
  import numpy.testing
  import pytest
  from pandas.testing import assert_frame_equal
  from lifelines import statistics
  from lifelines import CoxPHFitter, KaplanMeierFitter, WeibullFitter
  from lifelines.exceptions import StatisticalWarning
  from lifelines.datasets import load_waltons, load_g3, load_lymphoma, load_dd, load_regression_dataset, load_leukemia
label_desc: Use image node_id 40 for calling a global function with example usage: test_survival_difference_at_fixed_point_in_time_test_left_censoring() without return types