code: def talos_version():
      def mnist_model(x_train, y_train, x_val, y_val, params):
          model = Sequential()
          model.add(
              Conv2D(
                  32,
                  kernel_size=(3, 3),
                  activation=params["activation"],
                  input_shape=(28, 28, 1),
              )
          )
          model.add(Flatten())
          model.add(Dense(128, activation=params["activation"]))
          model.add(Dropout(params["dropout"]))
          model.add(Dense(10, activation="softmax"))
  
          model.compile(
              optimizer=params["optimizer"],
              loss=params["losses"],
              metrics=["acc", talos.utils.metrics.f1score],
          )
  
          out = model.fit_generator(
              SequenceGenerator(
                  x_train, y_train, batch_size=params["batch_size"]
              ),
              epochs=params["epochs"],
              validation_data=[x_val, y_val],
              callbacks=[],
              workers=4,
              verbose=0,
          )
  
          return out, model
  
      scan_object = talos.Scan(
          x=x_train,
          y=y_train,
          x_val=x_val,
          y_val=y_val,
          params=p,
          model=mnist_model,
          experiment_name="mnist",
          save_weights=False,
      )
  
imports: import talos
  from talos.utils import SequenceGenerator
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout, Flatten
  from tensorflow.keras.layers import Conv2D
label_desc: Use image node_id 1 for calling a global function with example usage: talos_version() and returns: out,  model