Adding tf_export decorators/calls to TensorFlow functions and constants.

PiperOrigin-RevId: 182862075

1 files changed, 10 insertions, 1 deletions

diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index 1facb8b1f2..c52d5fff5d 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -40,6 +40,7 @@ from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_contextlib
+from tensorflow.python.util.tf_export import tf_export
 
 __all__ = ["AUTO_REUSE", "VariableScope", "get_variable_scope",
            "get_variable", "get_local_variable", "variable_scope",
@@ -186,6 +187,7 @@ class _ReuseMode(enum.Enum):
   # REUSE_TRUE = 3
 
 AUTO_REUSE = _ReuseMode.AUTO_REUSE
+tf_export("AUTO_REUSE").export_constant(__name__, "AUTO_REUSE")
 AUTO_REUSE.__doc__ = """
 When passed in as the value for the `reuse` flag, AUTO_REUSE indicates that
 get_variable() should create the requested variable if it doesn't exist or, if
@@ -853,12 +855,14 @@ class _VariableStore(object):
 
 
 # To stop regularization, use this regularizer
+@tf_export("no_regularizer")
 def no_regularizer(_):
   """Use this function to prevent regularization of variables."""
   return None
 
 
 # TODO(alive): support caching devices and partitioned variables in Eager mode.
+@tf_export("VariableScope")
 class VariableScope(object):
   """Variable scope object to carry defaults to provide to `get_variable`.
 
@@ -1158,6 +1162,7 @@ _VARSTORE_KEY = ("__variable_store",)
 _VARSCOPE_KEY = ("__varscope",)
 
 
+@tf_export("get_variable_scope")
 def get_variable_scope():
   """Returns the current variable scope."""
   scope = ops.get_collection(_VARSCOPE_KEY)
@@ -1238,6 +1243,7 @@ class EagerVariableStore(object):
     # pylint: enable=protected-access
 
 
+@tf_export("get_variable")
 def get_variable(name,
                  shape=None,
                  dtype=None,
@@ -1349,6 +1355,7 @@ get_variable.__doc__ = get_variable_or_local_docstring % (
 
 
 @functools.wraps(get_variable)
+@tf_export("get_local_variable")
 def get_local_variable(*args, **kwargs):
   kwargs["trainable"] = False
   if "collections" in kwargs:
@@ -1663,7 +1670,8 @@ def _get_unique_variable_scope(prefix):
 # Named like a function for backwards compatibility with the
 # @tf_contextlib.contextmanager version, which was switched to a class to avoid
 # some object creation overhead.
-class variable_scope(object):  # pylint: disable=invalid-name
+@tf_export("variable_scope")  # pylint: disable=invalid-name
+class variable_scope(object):
   """A context manager for defining ops that creates variables (layers).
 
   This context manager validates that the (optional) `values` are from the same
@@ -1996,6 +2004,7 @@ class variable_scope(object):  # pylint: disable=invalid-name
 
 
 # pylint: disable=g-doc-return-or-yield
+@tf_export("variable_op_scope")
 @tf_contextlib.contextmanager
 def variable_op_scope(values,
                       name_or_scope,