[tune] Remove unused TF loggers (#7090)

doc/source/tune-usage.rst

@@ -618,21 +618,6 @@ You can pass in your own logging mechanisms to output logs in custom formats as
 
 These loggers will be called along with the default Tune loggers. All loggers must inherit the `Logger interface <tune-package-ref.html#ray.tune.logger.Logger>`__. Tune enables default loggers for Tensorboard, CSV, and JSON formats. You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details. An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
 
-TBXLogger (TensorboardX)
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-Tune provides a logger using `TensorBoardX <https://github.com/lanpa/tensorboardX>`_. You can install tensorboardX via ``pip install tensorboardX`` or ``pip install 'ray[tune]'``. This logger automatically outputs loggers similar to using a default TensorFlow logging format. By default, it will log any scalar value provided via the result dictionary along with HParams information.
-
-.. code-block:: python
-
-    from ray.tune.logger import TBXLogger
-
-    tune.run(
-        MyTrainableClass,
-        name="experiment_name",
-        loggers=[TBXLogger]
-    )
-
 MLFlow
 ~~~~~~