code: def l_1st(alpha):
      def loss_1st(y_true, y_pred):
          L = y_true
          Y = y_pred
          batch_size = tf.to_float(K.shape(L)[0])
          return (
              alpha
              * 2
              * tf.linalg.trace(
                  tf.matmul(tf.matmul(Y, L, transpose_a=True), Y)
              )
              / batch_size
          )
  
      return loss_1st
  
imports: import time
  import numpy
  import scipy.sparse
  import tensorflow
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.callbacks import History
  from tensorflow.python.keras.layers import Dense, Input
  from tensorflow.python.keras.models import Model
  from tensorflow.python.keras.regularizers import l1_l2
  from ..utils import preprocess_nxgraph
label_desc: Use image node_id 2 for calling a global function with example usage: l_1st(alpha) and returns: loss_1st, unknown