Remove usage of magic-api-link syntax from source files.

Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.

By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist`  will not get a link).

There is also no way, not to set custom link text. That's okay.

This is the result of the following regex replacement (+ a couple of manual edits.):

re: @\{([^$].*?)(\$.+?)?}
sub: `\1`

Which does the following replacements:

"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"

PiperOrigin-RevId: 208042358

1 files changed, 3 insertions, 3 deletions

diff --git a/tensorflow/contrib/image/python/ops/sparse_image_warp.py b/tensorflow/contrib/image/python/ops/sparse_image_warp.py
index 54a215d6db..1ea8f705b7 100644
--- a/tensorflow/contrib/image/python/ops/sparse_image_warp.py
+++ b/tensorflow/contrib/image/python/ops/sparse_image_warp.py
@@ -112,10 +112,10 @@ def sparse_image_warp(image,
   Apply a non-linear warp to the image, where the warp is specified by
   the source and destination locations of a (potentially small) number of
   control points. First, we use a polyharmonic spline
-  (@{tf.contrib.image.interpolate_spline}) to interpolate the displacements
+  (`tf.contrib.image.interpolate_spline`) to interpolate the displacements
   between the corresponding control points to a dense flow field.
   Then, we warp the image using this dense flow field
-  (@{tf.contrib.image.dense_image_warp}).
+  (`tf.contrib.image.dense_image_warp`).
 
   Let t index our control points. For regularization_weight=0, we have:
   warped_image[b, dest_control_point_locations[b, t, 0],
@@ -126,7 +126,7 @@ def sparse_image_warp(image,
   For regularization_weight > 0, this condition is met approximately, since
   regularized interpolation trades off smoothness of the interpolant vs.
   reconstruction of the interpolant at the control points.
-  See @{tf.contrib.image.interpolate_spline} for further documentation of the
+  See `tf.contrib.image.interpolate_spline` for further documentation of the
   interpolation_order and regularization_weight arguments.