message: Update documentation of tf.vectorized_map to use tf.random.uniform instead of tf.random_uniform since latter is not available in 2.0.
diff: @@ -361,8 +361,8 @@ def vectorized_map(fn, elems):
  loss = tf.nn.l2_loss(label - prediction)
  return g.gradient(loss, (layer.kernel, layer.bias))
  - inputs = tf.random_uniform([batch_size, num_features])
  - labels = tf.random_uniform([batch_size, 1])
  + inputs = tf.random.uniform([batch_size, num_features])
  + labels = tf.random.uniform([batch_size, 1])
  per_example_gradients = tf.vectorized_map(model_fn, (inputs, labels))
  assert per_example_gradients[0].shape == (batch_size, num_features, 1)
  assert per_example_gradients[1].shape == (batch_size, 1)
  