code: def titanic(x_train, y_train, x_val, y_val, params):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dropout, Dense
  
      # note how instead of passing the value, we pass a dictionary entry
      model = Sequential()
      model.add(
          Dense(
              params["first_neuron"],
              input_dim=x_train.shape[1],
              activation="relu",
          )
      )
  
      # same here, just passing a dictionary entry
      model.add(Dropout(params["dropout"]))
  
      # again, instead of the activation name, we have a dictionary entry
      model.add(Dense(1, activation=params["last_activation"]))
  
      # here are using a learning rate boundary
      model.compile(
          optimizer=params["optimizer"],
          loss=params["losses"],
          metrics=["acc"],
      )
  
      # here we are also using the early_stopper function for a callback
      out = model.fit(
          x=x_train,
          y=y_train,
          batch_size=params["batch_size"],
          epochs=params["epochs"],
          verbose=0,
          validation_data=(x_val, y_val),
      )
  
      return out, model
  
label_desc: Use image node_id 3 for calling a global function with example usage: titanic(x_train, y_train, x_val, y_val, params) and returns: out,  model