code: def test_SensitivityRandomCause():
      y, X, treatment, tau, b, e = synthetic_data(
          mode=1, n=100000, p=NUM_FEATURES, sigma=1.0
      )
  
      # generate the dataset format for sensitivity analysis
      INFERENCE_FEATURES = [
          "feature_" + str(i) for i in range(NUM_FEATURES)
      ]
      df = pd.DataFrame(X, columns=INFERENCE_FEATURES)
      df[TREATMENT_COL] = treatment
      df[OUTCOME_COL] = y
      df[SCORE_COL] = e
  
      # calling the Base XLearner class and return the sensitivity analysis summary report
      learner = BaseXLearner(LinearRegression())
      sens = SensitivityRandomCause(
          df=df,
          inference_features=INFERENCE_FEATURES,
          p_col=SCORE_COL,
          treatment_col=TREATMENT_COL,
          outcome_col=OUTCOME_COL,
          learner=learner,
      )
  
      sens_summary = sens.summary(method="Random Cause")
      print(sens_summary)
  
file_path: causalml/tests/test_sensitivity.py
imports: import pandas
  import numpy
  from sklearn.linear_model import LinearRegression
  from causalml.dataset import synthetic_data
  from causalml.inference.meta import BaseXLearner
  from causalml.metrics.sensitivity import Sensitivity
  from causalml.metrics.sensitivity import SensitivityPlaceboTreatment, SensitivityRandomCause
  from causalml.metrics.sensitivity import SensitivityRandomReplace, SensitivitySelectionBias
  from causalml.metrics.sensitivity import one_sided, alignment, one_sided_att, alignment_att
  from .const import TREATMENT_COL, SCORE_COL, OUTCOME_COL, NUM_FEATURES
label_desc: Use image node_id 3 for calling a global function with example usage: test_SensitivityRandomCause() without return types