Update tutorials/docs-10-using-turing-autodiff/index.qmd

tutorials/docs-10-using-turing-autodiff/index.qmd

@@ -20,7 +20,8 @@ As of Turing version v0.30, the global configuration flag for the AD backend has
 Users can pass the `adtype` keyword argument to the sampler constructor to select the desired AD backend, with the default being `AutoForwardDiff(; chunksize=0)`.
 
 For `ForwardDiff`, pass `adtype=AutoForwardDiff(; chunksize)` to the sampler constructor. A `chunksize` of 0 permits the chunk size to be automatically determined. For more information regarding the selection of `chunksize`, please refer to [related section of `ForwardDiff`'s documentation](https://juliadiff.org/ForwardDiff.jl/dev/user/advanced/#Configuring-Chunk-Size).
-For `ReverseDiff`, pass `adtype=AutoReverseDiff()` to the sampler constructor. An additional argument can be provided to `AutoReverseDiff` to specify whether to to cache the tape only once and reuse it later use (`false` by default, which means no caching tape). This can substantially improve performance, but risks silently incorrect results if not used with care.
+For `ReverseDiff`, pass `adtype=AutoReverseDiff()` to the sampler constructor. An additional argument can be provided to `AutoReverseDiff` to specify whether to to cache the tape only once and reuse it later use (`false` by default, which means no caching). This can substantially improve performance, but risks silently incorrect results if not used with care.
+
 
 Cached tapes should only be used if you are absolutely certain that the sequence of operations performed in your code does not change between different executions of your model.
 Thus, e.g., in the model definition and all implicitly and explicitly called functions in the model, all loops should be of fixed size, and `if`-statements should consistently execute the same branches.