input: def generate_mnist_model() -> str:
      """Generate a TensorFlow model for classifying hand-written digits from the MNIST dataset.
      The model should be a multilayer perceptron with two hidden layers, each having 256 neurons.
      The model should be trained for 15 epochs and should output the final accuracy on the test set.
      >>> generate_mnist_model()
      'Optimization Finished!\\nAccuracy: 0.9453'
      """
      import tensorflow as tf
      # Import MNIST data
      from tensorflow.examples.tutorials.mnist import input_data
      mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
  
      # Parameters
      learning_rate = 0.001
      training_epochs = 15
      batch_size = 100
      display_step = 1
  
      # Network Parameters
      n_hidden_1 = 256 # 1st layer number of neurons
      n_hidden_2 = 256 # 2nd layer number of neurons
      n_input = 784 # MNIST data input (img shape: 28*28)
      n_classes = 10 # MNIST total classes (0-9 digits)
  
      # tf Graph input
      X = tf.placeholder("float", [None, n_input])
      Y = tf.placeholder("float", [None, n_classes])
  
      # Store layers weight & bias
      weights = {
          'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
          'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
          'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
      }
      biases = {
          'b1': tf.Variable(tf.random_normal([n_hidden_1])),
          'b2': tf.Variable(tf.random_normal([n_hidden_2])),
          'out': tf.Variable(tf.random_normal([n_classes]))
      }
  
      # Create model
      def multilayer_perceptron(x):
          # Hidden fully connected layer with 256 neurons
          layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
          # Hidden fully connected layer with 256 neurons
          layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
          # Output fully connected layer with a neuron for each class
          out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
          return out_layer
  
      # Construct model
      logits = multilayer_perceptron(X)
  
      # Define loss and optimizer
      loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
          logits=logits, labels=Y))
      optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
      train_op = optimizer.minimize(loss_op)
  
      # Initializing the variables
      init = tf.global_variables_initializer()
  
      # Train model
      with tf.Session() as model:
          model.run(init)
  
          # Training cycle
          for epoch in range(training_epochs):
              avg_cost = 0.
              total_batch = int(mnist.train.num_examples/batch_size)
              # Loop over all batches
              for i in range(total_batch):
                  batch_x, batch_y = mnist.train.next_batch(batch_size)
                  # Run optimization op (backprop) and cost op (to get loss value)
                  _, c = model.run([train_op, loss_op], feed_dict={X: batch_x,
                                                                  Y: batch_y})
                  # Compute average loss
                  avg_cost += c / total_batch
              # Display logs per epoch step
              if epoch % display_step == 0:
                  print("Epoch:", '%04d' % (epoch+1), "cost=", \
                      "{:.9f}".format(avg_cost))
          print("Optimization Finished!")
  
          # Test model
          pred = tf.nn.softmax(logits)  # Apply softmax to logits
          correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
          # Calculate accuracy
          accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
          return f"Optimization Finished!\\nAccuracy: {accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})}"
output:     
          # Test model
          pred = tf.nn.softmax(logits)  # Apply softmax to logits
          correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
          # Calculate accuracy
          accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
          return f"Optimization Finished!\\nAccuracy: {accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})}"
  