input: 
  import tensorflow as tf
  
  def mnist_classification_model() -> tf.Session:
      """Generate a neural network model using TensorFlow for classifying hand-written digits from the MNIST dataset.
      The function initializes and trains the model, returning the trained session object.
      
      >>> model = mnist_classification_model()
      Epoch: 0001 cost= 173.569402411
      Epoch: 0002 cost= 40.739649517
      Epoch: 0003 cost= 26.465736874
      Epoch: 0004 cost= 19.097779739
      Epoch: 0005 cost= 14.666652493
      Epoch: 0006 cost= 11.259115680
      Epoch: 0007 cost= 9.263545613
      Epoch: 0008 cost= 7.452419269
      Epoch: 0009 cost= 5.881298159
      Epoch: 0010 cost= 4.883464733
      Epoch: 0011 cost= 3.791618973
      Epoch: 0012 cost= 2.900145390
      Epoch: 0013 cost= 2.516648715
      Epoch: 0014 cost= 1.810532651
      Epoch: 0015 cost= 1.647086363
      Optimization Finished!
      Accuracy: 0.952
      """
      # 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))
      
      return model
  
output:     model.close()