input: 
  def train_mnist_classifier(epochs: int, batch_size: int, learning_rate: float) -> float:
      """ Train a neural network model using TensorFlow to classify hand-written digits from the MNIST dataset.
      The function initializes the model, trains it for the specified number of epochs, and returns the accuracy of the model on the test set.
      >>> train_mnist_classifier(15, 100, 0.001)
      0.95
      """
      import tensorflow as tf
      from tensorflow.examples.tutorials.mnist import input_data
  
      mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
  
      n_hidden_1 = 256
      n_hidden_2 = 256
      n_input = 784
      n_classes = 10
  
      X = tf.placeholder("float", [None, n_input])
      Y = tf.placeholder("float", [None, n_classes])
  
      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]))
      }
  
      def multilayer_perceptron(x):
          layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
          layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
          out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
          return out_layer
  
      logits = multilayer_perceptron(X)
      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)
  
      init = tf.global_variables_initializer()
      model = tf.Session()
      model.run(init)
  
      for epoch in range(epochs):
          avg_cost = 0.
          total_batch = int(mnist.train.num_examples / batch_size)
          for i in range(total_batch):
              batch_x, batch_y = mnist.train.next_batch(batch_size)
              _, c = model.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})
              avg_cost += c / total_batch
  
      pred = tf.nn.softmax(logits)
      correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
      return accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}, session=model)
  
output:     
      model.close()
  