Update function.ipynb

fixed two typos

Author: NinoRisteski

site/en/guide/function.ipynb

@@ -362,7 +362,7 @@
         "\n",
         "When called, a `Function` matches the call arguments to existing `ConcreteFunction`s using `tf.types.experimental.TraceType` of each argument. If a matching `ConcreteFunction` is found, the call is dispatched to it. If no match is found, a new `ConcreteFunction` is traced.\n",
         "\n",
-        "If multiple matches are found, the most specific signature is chosen. Matching is done by [subtyping](https://en.wikipedia.org/wiki/Subtyping), much like normal function calls in C++ or Java, for instance. For example, `TensorShape([1, 2])` is a subtype of `TensorShape([None, None])` and so a call to the tf.function with `TensorShape([1, 2])` can be dispatched to the `ConcreteFunction` produced with `TensorShape([None, None])` but if a `ConcreteFunction` with `TensorShape([1, None])` also exists then it will prioritized since it is more specific.\n",
+        "If multiple matches are found, the most specific signature is chosen. Matching is done by [subtyping](https://en.wikipedia.org/wiki/Subtyping), much like normal function calls in C++ or Java, for instance. For example, `TensorShape([1, 2])` is a subtype of `TensorShape([None, None])` and so a call to the tf.function with `TensorShape([1, 2])` can be dispatched to the `ConcreteFunction` produced with `TensorShape([None, None])` but if a `ConcreteFunction` with `TensorShape([1, None])` also exists then it will be prioritized since it is more specific.\n",
         "\n",
         "The `TraceType` is determined from input arguments as follows:\n",
         "* For `Tensor`, the type is parameterized by the `Tensor`'s `dtype` and `shape`; ranked shapes are a subtype of unranked shapes; fixed dimensions are a subtype of unknown dimensions\n",
@@ -395,7 +395,7 @@
       "source": [
         "### Controlling retracing\n",
         "\n",
-        "Retracing, which is when your `Function` creates more than one trace, helps ensures that TensorFlow generates correct graphs for each set of inputs. However, tracing is an expensive operation! If your `Function` retraces a new graph for every call, you'll find that your code executes more slowly than if you didn't use `tf.function`.\n",
+        "Retracing, which is when your `Function` creates more than one trace, helps ensure that TensorFlow generates correct graphs for each set of inputs. However, tracing is an expensive operation! If your `Function` retraces a new graph for every call, you'll find that your code executes more slowly than if you didn't use `tf.function`.\n",
         "\n",
         "To control the tracing behavior, you can use the following techniques:"
       ]