code: def main_diabetes():
      master_seed(1234)
  
      model = Sequential()
      model.add(Dense(100, activation="relu", input_dim=10))
      model.add(Dense(10, activation="relu"))
      model.add(Dense(1))
  
      model.compile(
          loss="mean_squared_error",
          optimizer=tf.keras.optimizers.Adam(lr=0.01),
          metrics=["accuracy"],
      )
  
      (x_train, y_train), (_, _), _, _ = load_dataset("diabetes")
  
      model.fit(x_train, y_train, batch_size=128, epochs=10)
  
      w_0, b_0 = model.layers[0].get_weights()
      w_1, b_1 = model.layers[1].get_weights()
      w_2, b_2 = model.layers[2].get_weights()
  
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/",
              "W_DENSE1_DIABETES",
          ),
          w_0,
      )
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/" "B_DENSE1_DIABETES",
          ),
          b_0,
      )
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/" "W_DENSE2_DIABETES",
          ),
          w_1,
      )
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/" "B_DENSE2_DIABETES",
          ),
          b_1,
      )
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/" "W_DENSE3_DIABETES",
          ),
          w_2,
      )
      np.save(
          os.path.join(
              os.path.dirname(os.path.dirname(__file__)),
              "resources/models/" "B_DENSE3_DIABETES",
          ),
          b_2,
      )
  
file_path: adversarial-robustness-toolbox/utils/resources/create_model_weights.py
imports: import os
  import pickle
  import tensorflow
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
  from sklearn.linear_model import LogisticRegression
  from sklearn.svm import SVC, LinearSVC
  from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
  from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier
  from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
  import numpy
  from tests.utils import load_dataset, master_seed
label_desc: Use image node_id 2 for calling a global function with example usage: main_diabetes() without return types