input: Write a function to generate a TensorFlow neural network model for classifying hand-written digits from the MNIST dataset. The function should train the model using the parameters provided and return the trained model.
  assert generate_mnist_model[()].accuracy == 0.95
  assert generate_mnist_model[()].epochs == 15
  assert generate_mnist_model[()].learning_rate == 0.001
  assert generate_mnist_model[()].batch_size == 100
  assert generate_mnist_model[()].display_step == 1
output: 
  def generate_mnist_model():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape((x_train.shape[0], 28, 28, 1)).astype('float32') / 255
      x_test = x_test.reshape((x_test.shape[0], 28, 28, 1)).astype('float32') / 255
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      model = models.Sequential([
          layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.Flatten(),
          layers.Dense(64, activation='relu'),
          layers.Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(x_train, y_train, epochs=15, batch_size=100, validation_data=(x_test, y_test), verbose=1)
  
      return model
  