code: def recover_best_model():
      import os
  
      from talos.utils import recover_best_model
      import talos
      import tensorflow
  
      experiment_log = "test_q/" + os.listdir("test_q")[0]
  
      x, y = talos.templates.datasets.iris()
      input_model = talos.templates.models.iris
  
      x = x[:50]
      y = y[:50]
  
      # define the input model
      def iris_model(x_train, y_train, x_val, y_val, params):
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Dense
  
          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,
              batch_size=params["batch_size"],
              epochs=params["epochs"],
              validation_data=(x_val, y_val),
              verbose=0,
          )
  
          return out, model
  
      recover_best_model(
          x_train=x,
          y_train=y,
          x_val=x,
          y_val=y,
          experiment_log=experiment_log,
          input_model=iris_model,
          metric="acc",
          x_cross=x,
          y_cross=y,
          n_models=5,
          task="multi_label",
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: recover_best_model() and returns: out,  model