*   `tf.function`:

    *   `tf.function` now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
        *   Using `functools.wraps` on a function with different signature

Therefore, typical uses of captured tensors should involve values which remain
unchanged across session.run calls. Inference is a good example of this.

SparseTensor is not supported. The return value of the decorated function
must be a Tensor or a list/tuple of Tensors.
  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{The tensors to be batched.}]>:$in_tensors,