code: def support_recall(x_truth, x, eps=1e-8):
      """Computes proportion of true positives (TP) among the number ground
      truth positives (namely TP + FN, where FN is the number of false
      negatives), hence TP / (TP + FN). This is useful to assess
      the features selection or outliers detection abilities of a learner.
  
      Parameters
      ----------
      x_truth : `numpy.array`
          Ground truth weights
  
      x : `numpy.array`
          Learned weights
  
      Returns
      -------
      output : `float`
          The False Discovery Proportion for detecting the support of ``x_truth``
          using the support of ``x``
      """
      s = np.abs(x) > eps
      s_truth = np.abs(x_truth) > eps
      v = np.logical_and(s_truth, s).sum()
      return v / s_truth.sum()
  
label_desc: Use image node_id 2 for calling a global function with example usage: support_recall(x_truth, x, eps) and returns: unknown