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-rw-r--r--tensorflow/docs_src/extend/new_data_formats.md10
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/docs_src/extend/new_data_formats.md b/tensorflow/docs_src/extend/new_data_formats.md
index 47a8344b70..7ca50c9c76 100644
--- a/tensorflow/docs_src/extend/new_data_formats.md
+++ b/tensorflow/docs_src/extend/new_data_formats.md
@@ -4,7 +4,7 @@ PREREQUISITES:
* Some familiarity with C++.
* Must have
- @{$install_sources$downloaded TensorFlow source}, and be
+ [downloaded TensorFlow source](../install/install_sources.md), and be
able to build it.
We divide the task of supporting a file format into two pieces:
@@ -67,7 +67,7 @@ need to:
You can put all the C++ code in a single file, such as
`my_reader_dataset_op.cc`. It will help if you are
-familiar with @{$adding_an_op$the adding an op how-to}. The following skeleton
+familiar with [the adding an op how-to](../extend/adding_an_op.md). The following skeleton
can be used as a starting point for your implementation:
```c++
@@ -227,8 +227,8 @@ REGISTER_KERNEL_BUILDER(Name("MyReaderDataset").Device(tensorflow::DEVICE_CPU),
```
The last step is to build the C++ code and add a Python wrapper. The easiest way
-to do this is by @{$adding_an_op#build_the_op_library$compiling a dynamic
-library} (e.g. called `"my_reader_dataset_op.so"`), and adding a Python class
+to do this is by [compiling a dynamic
+library](../extend/adding_an_op.md#build_the_op_library) (e.g. called `"my_reader_dataset_op.so"`), and adding a Python class
that subclasses `tf.data.Dataset` to wrap it. An example Python program is
given here:
@@ -285,7 +285,7 @@ You can see some examples of `Dataset` wrapper classes in
## Writing an Op for a record format
Generally this is an ordinary op that takes a scalar string record as input, and
-so follow @{$adding_an_op$the instructions to add an Op}.
+so follow [the instructions to add an Op](../extend/adding_an_op.md).
You may optionally take a scalar string key as input, and include that in error
messages reporting improperly formatted data. That way users can more easily
track down where the bad data came from.