code: def test_latest():
      import warnings
  
      warnings.simplefilter("ignore")
  
      print("\n >>> start Latest Features... \n")
  
      import talos
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      x, y = talos.templates.datasets.iris()
  
      p = {
          "activation": ["relu", "elu"],
          "optimizer": ["Nadam", "Adam"],
          "losses": ["logcosh"],
          "shapes": ["brick"],
          "first_neuron": [16, 32, 64, 128],
          "hidden_layers": [0, 1, 2, 3],
          "dropout": [0.2, 0.3, 0.4],
          "batch_size": [20, 30, 40, 50],
          "epochs": [10],
      }
  
      from talos.parameters.DistributeParamSpace import (
          DistributeParamSpace,
      )
  
      param_spaces = DistributeParamSpace(
          params=p, param_keys=p.keys(), machines=100
      )
  
      def iris_model(x_train, y_train, x_val, y_val, params):
          model = Sequential()
          model.add(
              Dense(
                  params["first_neuron"],
                  input_dim=4,
                  activation=params["activation"],
              )
          )
  
          talos.utils.hidden_layers(model, params, 3)
  
          model.add(Dense(3, activation="softmax"))
          model.compile(
              optimizer=params["optimizer"],
              loss=params["losses"],
              metrics=["acc"],
          )
  
          out = model.fit(
              x_train,
              y_train,
              callbacks=[
                  talos.callbacks.ExperimentLog("test_latest", params)
              ],
              batch_size=params["batch_size"],
              epochs=params["epochs"],
              validation_data=(x_val, y_val),
              verbose=0,
          )
  
          return out, model
  
      print(type(param_spaces.param_spaces[0]))
  
      scan_object = talos.Scan(
          x,
          y,
          model=iris_model,
          params=param_spaces.param_spaces[0],
          experiment_name="test_latest",
          round_limit=5,
          reduction_method="gamify",
          save_models=True,
      )
  
      scan_object.best_model(saved=True)
      predict = talos.Predict(scan_object)
      predict.predict(x, "val_acc", False, saved=True)
  
      scan_object = talos.Scan(
          x,
          y,
          model=iris_model,
          params=p,
          experiment_name="test_latest",
          round_limit=5,
          reduction_method="gamify",
          save_weights=False,
      )
  
      print("finised Latest Features \n")
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_latest() and returns: out,  model