Crash when type cannot be specialized in Tensorflow

TensorFlow can crash when it fails to handle certain data type transformations during model processing. This happens because the code doesn't properly check for errors before trying to use the result, causing the application to stop unexpectedly.

During shape inference, TensorFlow fails to specialize types in specific scenarios where DCHECK assertions (which are disabled in production builds) do not catch the error condition. Execution then proceeds to call ValueOrDie() on an error Status object rather than a valid value, resulting in a crash. The vulnerability affects production deployments when malformed models or specific input configurations trigger the unhandled type specialization failure.