code: def iris_model(x_train, y_train, x_val, y_val, params):
      model = Sequential()
      model.add(
          Dense(
              params["first_neuron"],
              input_dim=x_train.shape[1],
              activation="relu",
          )
      )
      model.add(Dropout(0.5))
      model.add(Dense(y_train.shape[1], activation="softmax"))
  
      model.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["acc"],
      )
  
      out = model.fit(
          x_train,
          y_train,
          batch_size=params["batch_size"],
          epochs=50,
          verbose=0,
          validation_data=[x_val, y_val],
      )
  
      return out, model
  
imports: from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.losses import categorical_crossentropy
  from tensorflow.keras.activations import relu, softmax
  import talos
label_desc: Use image node_id 1 for calling a global function with example usage: iris_model(x_train, y_train, x_val, y_val, params) and returns: out,  model