code: def create_model(node_size, hidden_size=[256, 128], l1=1e-5, l2=1e-4):
      A = Input(shape=(node_size,))
      L = Input(shape=(None,))
      fc = A
      for i in range(len(hidden_size)):
          if i == len(hidden_size) - 1:
              fc = Dense(
                  hidden_size[i],
                  activation="relu",
                  kernel_regularizer=l1_l2(l1, l2),
                  name="1st",
              )(fc)
          else:
              fc = Dense(
                  hidden_size[i],
                  activation="relu",
                  kernel_regularizer=l1_l2(l1, l2),
              )(fc)
      Y = fc
      for i in reversed(range(len(hidden_size) - 1)):
          fc = Dense(
              hidden_size[i],
              activation="relu",
              kernel_regularizer=l1_l2(l1, l2),
          )(fc)
  
      A_ = Dense(node_size, "relu", name="2nd")(fc)
      model = Model(inputs=[A, L], outputs=[A_, Y])
      emb = Model(inputs=A, outputs=Y)
      return model, emb
  
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 3 for calling a global function with example usage: create_model(node_size, hidden_size, l1, l2) and returns: model,  emb