code: def confusion_matrix(
      y_target, y_predicted, binary=False, positive_label=1
  ):
      """Compute a confusion matrix/contingency table.
  
      Parameters
      -----------
      y_target : array-like, shape=[n_samples]
          True class labels.
      y_predicted : array-like, shape=[n_samples]
          Predicted class labels.
      binary : bool (default: False)
          Maps a multi-class problem onto a
          binary confusion matrix, where
          the positive class is 1 and
          all other classes are 0.
      positive_label : int (default: 1)
          Class label of the positive class.
  
      Returns
      ----------
      mat : array-like, shape=[n_classes, n_classes]
  
      Examples
      -----------
      For usage examples, please see
      https://rasbt.github.io/mlxtend/user_guide/evaluate/confusion_matrix/
  
      """
      if not isinstance(y_target, np.ndarray):
          targ_tmp = np.asarray(y_target)
      else:
          targ_tmp = y_target
      if not isinstance(y_predicted, np.ndarray):
          pred_tmp = np.asarray(y_predicted)
      else:
          pred_tmp = y_predicted
  
      if len(y_target) != len(y_predicted):
          raise AttributeError(
              "`y_target` and `y_predicted`"
              " don't have the same number of elements."
          )
  
      if binary:
          targ_tmp = np.where(targ_tmp != positive_label, 0, 1)
          pred_tmp = np.where(pred_tmp != positive_label, 0, 1)
  
      class_labels = np.unique(np.concatenate((targ_tmp, pred_tmp)))
      if class_labels.shape[0] == 1:
          if class_labels[0] != 0:
              class_labels = np.array([0, class_labels[0]])
          else:
              class_labels = np.array([class_labels[0], 1])
      n_labels = class_labels.shape[0]
      lst = []
      z = list(zip(targ_tmp, pred_tmp))
      for combi in product(class_labels, repeat=2):
          lst.append(z.count(combi))
      mat = np.asarray(lst)[:, None].reshape(n_labels, n_labels)
      return mat
  
label_desc: Use image node_id 1 for calling a global function with example usage: confusion_matrix(y_target, y_predicted, binary, positive_label) and returns: mat