code: def track_epsilon(concat_true, concat_pred):
      """
      Tracks the mean absolute value of epsilon.
  
      Args:
          - concat_true (tf.tensor): tensor of true samples, with shape (n_samples, 2)
                                     Each row in concat_true is comprised of (y, treatment)
          - concat_pred (tf.tensor): tensor of predictions, with shape (n_samples, 4)
                                     Each row in concat_pred is comprised of (y0, y1, propensity, epsilon)
      Returns:
          - (float): mean absolute value of epsilon
      """
      epsilons = concat_pred[:, 3]
      return tf.abs(tf.reduce_mean(epsilons))
  
file_path: causalml/causalml/inference/tf/utils.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Layer
  from tensorflow.keras.metrics import binary_accuracy
label_desc: Use image node_id 5 for calling a global function with example usage: track_epsilon(concat_true, concat_pred) and returns: tf