input: def generate_mnist_model():
      """
      Generate a neural network model using TensorFlow and Python for classifying hand-written digits from the MNIST dataset. 
      This function sets up the network architecture, defines the loss and optimization functions, and trains the model.
      The function does not take any input arguments but initializes necessary parameters and loads the MNIST dataset.
      After training, it evaluates the model on the test dataset and prints the accuracy.
  
      Example:
      >>> generate_mnist_model()
      Epoch: 0001 cost= 1.163085938
      Epoch: 0002 cost= 0.432801465
      Epoch: 0003 cost= 0.336817648
      ...
      Epoch: 0015 cost= 0.098765432
      Optimization Finished!
      Accuracy: 0.92
      """
      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"))
          print("Accuracy:", 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"))
          print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))
  