input: Write a python function to create and train a neural network model using Keras.
  assert isinstance(create_and_train_model(np.array([[0.8, 0.4, 0.7]])), Sequential)
  assert create_and_train_model(np.array([[0.8, 0.4, 0.7]])).layers[0].output_shape == (None, 64)
  assert create_and_train_model(np.array([[0.8, 0.4, 0.7]])).layers[2].output_shape == (None, 1)
output: from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  import numpy as np
  
  def create_and_train_model(data):
      model = Sequential([
          Dense(64, activation='relu', input_shape=(data.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Dummy labels for the sake of example
      labels = np.random.randint(2, size=(data.shape[0], 1))
      
      model.fit(data, labels, epochs=10, batch_size=32)
      
      return model