code: def breast_cancer(x_train, y_train, x_val, y_val, params):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dropout, Dense
      from talos.model import (
          lr_normalizer,
          early_stopper,
          hidden_layers,
      )
  
      from talos.metrics.keras_metrics import (
          matthews,
          precision,
          recall,
          f1score,
      )
  
      model = Sequential()
      model.add(
          Dense(
              params["first_neuron"],
              input_dim=x_train.shape[1],
              activation="relu",
          )
      )
  
      model.add(Dropout(params["dropout"]))
  
      hidden_layers(model, params, 1)
  
      model.add(Dense(1, activation=params["last_activation"]))
  
      model.compile(
          optimizer=params["optimizer"](
              lr=lr_normalizer(params["lr"], params["optimizer"])
          ),
          loss=params["losses"],
          metrics=["acc", f1score, recall, precision, matthews],
      )
  
      results = model.fit(
          x_train,
          y_train,
          batch_size=params["batch_size"],
          epochs=params["epochs"],
          verbose=0,
          validation_data=(x_val, y_val),
          callbacks=[
              early_stopper(
                  params["epochs"],
                  mode="moderate",
                  monitor="val_f1score",
              )
          ],
      )
  
      return results, model
  
label_desc: Use image node_id 1 for calling a global function with example usage: breast_cancer(x_train, y_train, x_val, y_val, params) and returns: results,  model