code: def binary_clas_accuracy(pos_preds=None, neg_preds=None):
      """Calculates the accuracy of binary predictions.
  
      Args:
          pos_preds (optional): A Tensor of any shape containing the
              predicted values on positive data (i.e., ground truth labels are
              `1`).
          neg_preds (optional): A Tensor of any shape containing the
              predicted values on negative data (i.e., ground truth labels are
              `0`).
  
      Returns:
          A float scalar Tensor containing the accuracy.
      """
      pos_accu = accuracy(tf.ones_like(pos_preds), pos_preds)
      neg_accu = accuracy(tf.zeros_like(neg_preds), neg_preds)
      psize = tf.cast(tf.size(pos_preds), tf.float32)
      nsize = tf.cast(tf.size(neg_preds), tf.float32)
      accu = (pos_accu * psize + neg_accu * nsize) / (psize + nsize)
      return accu
  
label_desc: Use image node_id 2 for calling a global function with example usage: binary_clas_accuracy(pos_preds, neg_preds) and returns: accu