input: 
  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
  model = tf.Session()
  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"))
  print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}, model))
  
  What is the purpose of the `multilayer_perceptron` function in the provided TensorFlow code?
output:     # 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