Merge pull request #2269 from sushreebarsa:patch-21

PiperOrigin-RevId: 562804659

Author: copybara-github

site/en/guide/function.ipynb

@@ -61,7 +61,7 @@
         "id": "J122XQYG7W6w"
       },
       "source": [
-        "In TensorFlow 2, [eager execution](eager.ipynb) is turned on by default. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability.\n",
+        "In TensorFlow 2, [eager execution](basics.ipynb) is turned on by default. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability.\n",
         "\n",
         "You can use `tf.function` to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use `SavedModel`.\n",
         "\n",
@@ -440,7 +440,7 @@
       "source": [
         "#### Use unknown dimensions for flexibility\n",
         "\n",
-        "  Since TensorFlow matches tensors based on their shape, using a `None` dimension as a wildcard will allow `Function`s to reuse traces for variably-sized input. Variably-sized input can occur if you have sequences of different length, or images of different sizes for each batch (See the [Transformer](../tutorials/text/transformer.ipynb) and [Deep Dream](../tutorials/generative/deepdream.ipynb) tutorials for example)."
+        "  Since TensorFlow matches tensors based on their shape, using a `None` dimension as a wildcard will allow `Function`s to reuse traces for variably-sized input. Variably-sized input can occur if you have sequences of different length, or images of different sizes for each batch. You can check out the [Transformer](https://www.tensorflow.org/text/tutorials/transformer) and [Deep Dream](../tutorials/generative/deepdream.ipynb) tutorials for examples."
       ]
     },
     {
@@ -974,7 +974,7 @@
       "source": [
         "When wrapping Python/NumPy data in a Dataset, be mindful of `tf.data.Dataset.from_generator` versus ` tf.data.Dataset.from_tensor_slices`. The former will keep the data in Python and fetch it via `tf.py_function` which can have performance implications, whereas the latter will bundle a copy of the data as one large `tf.constant()` node in the graph, which can have memory implications.\n",
         "\n",
-        "Reading data from files via `TFRecordDataset`, `CsvDataset`, etc. is the most effective way to consume data, as then TensorFlow itself can manage the asynchronous loading and prefetching of data, without having to involve Python. To learn more, see the [`tf.data`: Build TensorFlow input pipelines](../../guide/data) guide."
+        "Reading data from files via `TFRecordDataset`, `CsvDataset`, etc. is the most effective way to consume data, as then TensorFlow itself can manage the asynchronous loading and prefetching of data, without having to involve Python. To learn more, see the [`tf.data`: Build TensorFlow input pipelines](data.ipynb) guide."
       ]
     },
     {