code: def alignment(alpha, p, treatment):
      """Alignment confounding function.
      Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
      for causal effects." Political Analysis 22.2 (2014): 169-182.
      https://www.mattblackwell.org/files/papers/causalsens.pdf
  
      Args:
          alpha (np.array): a confounding values vector
          p (np.array): a propensity score vector between 0 and 1
          treatment (np.array): a treatment vector (1 if treated, otherwise 0)
      """
  
      assert p.shape[0] == treatment.shape[0]
      adj = alpha * (1 - p) * treatment + alpha * p * (1 - treatment)
      return adj
  
imports: import logging
  import numpy
  import pandas
  from collections import defaultdict
  import matplotlib.pyplot
  from importlib import import_module
label_desc: Use image node_id 2 for calling a global function with example usage: alignment(alpha, p, treatment) and returns: adj