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, 9 insertions, 7 deletions

diff --git a/tensorflow/contrib/kernel_methods/README.md b/tensorflow/contrib/kernel_methods/README.md
index 44ed9670a0..1bce3277ff 100644
--- a/tensorflow/contrib/kernel_methods/README.md
+++ b/tensorflow/contrib/kernel_methods/README.md
@@ -21,13 +21,15 @@ Currently, there is a [RandomFourierFeatureMapper](https://www.tensorflow.org/co
 output. More mappers are on the way.
 
 ## Kernel-based Estimators
-These are estimators inheriting from the @{tf.contrib.learn.Estimator} class and
-use kernel mappers internally to discover non-linearities in the data. These
-canned estimators map their input features using kernel mapper Ops and then
-apply linear models to the mapped features. Combining kernel mappers with linear
-models and different loss functions leads to a variety of models: linear and
-non-linear SVMs, linear regression (with and without kernels) and (multinomial)
-logistic regression (with and without kernels).
+
+These estimators inherit from the
+[`tf.contrib.learn.Estimator`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/estimator.py)
+class and use kernel mappers internally to discover non-linearities in the
+data. These canned estimators map their input features using kernel mapper
+Ops and then apply linear models to the mapped features. Combining kernel
+mappers with linear models and different loss functions leads to a variety of
+models: linear and non-linear SVMs, linear regression (with and without
+kernels) and (multinomial) logistic regression (with and without kernels).
 
 Currently there is a [KernelLinearClassifier](https://www.tensorflow.org/code/tensorflow/contrib/kernel_methods/python/kernel_estimators.py) implemented but more pre-packaged estimators
 are on the way.