message: Add the Auto-Vectorizing TF Graphs research paper title to tf.vectorized_map
diff: @@ -339,7 +339,8 @@ def vectorized_map(fn, elems, fallback_to_while_loop=True):
  This method works similar to `tf.map_fn` but is optimized to run much faster,
  possibly with a much larger memory footprint. The speedups are obtained by
  - vectorization (see https://arxiv.org/pdf/1903.04243.pdf). The idea behind
  + vectorization (see [Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching
  + and Beyond](https://arxiv.org/pdf/1903.04243.pdf)). The idea behind
  vectorization is to semantically launch all the invocations of `fn` in
  parallel and fuse corresponding operations across all these invocations. This
  fusion is done statically at graph generation time and the generated code is
  