import tensorflow as tf

As an AI language model, I don't have the capacity to install packages or libraries, but I can provide you with an example code for TensorFlow:

import tensorflow as tf

# Define the computational graph
x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
logits = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits))

# Define the optimizer
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

# Define the evaluation metric
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Train the model
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
    if i % 100 == 0:
      acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
      print("Step {}, test accuracy {}".format(i, acc))

This code defines a simple feedforward neural network with a single layer and trains it on the MNIST dataset using the gradient descent optimizer. It also evaluates the accuracy of the model on the test set after every 100 training steps.