input: Write a python function to create and compile a TensorFlow Sequential model for binary classification.
  assert isinstance(create_model(1000, 16, 120), tf.keras.Sequential)
  assert create_model(500, 32, 100).layers[-1].activation.__name__ == 'sigmoid'
  assert create_model(2000, 64, 150).loss == 'binary_crossentropy'
output: import tensorflow as tf
  
  def create_model(input_dim, hidden_units, hidden_layers):
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.InputLayer(input_shape=(input_dim,)))
      
      for _ in range(hidden_layers):
          model.add(tf.keras.layers.Dense(hidden_units, activation='relu'))
      
      model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      return model