use common.DEBUG_MODE

tensorflow/python/data/ops/dataset_ops.py

@@ -36,7 +36,7 @@
 from tensorflow.python.compat import compat
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.data.ops import options as options_lib
-from tensorflow.python.data.ops.common import DEBUG_MODE
+from tensorflow.python.data.ops import common
 from tensorflow.python.data.ops.common import StructuredFunctionWrapper
 from tensorflow.python.data.ops.common import _should_unpack
 from tensorflow.python.data.util import nest
@@ -440,7 +440,7 @@ def options(self):
     return self._options_attr
 
   def _apply_debug_options(self):
-    if DEBUG_MODE:
+    if common.DEBUG_MODE:
       # Disable autotuning and static optimizations that could introduce
       # parallelism or asynchrony.
       options = options_lib.Options()
@@ -1255,7 +1255,7 @@ def prefetch(self, buffer_size, name=None):
     Returns:
       Dataset: A `Dataset`.
     """
-    if DEBUG_MODE:
+    if common.DEBUG_MODE:
       return self
     return PrefetchDataset(self, buffer_size, name=name)
 
@@ -1677,8 +1677,8 @@ def batch(self,
     Returns:
       Dataset: A `Dataset`.
     """
-    if num_parallel_calls is None or DEBUG_MODE:
-      if deterministic is not None and not DEBUG_MODE:
+    if num_parallel_calls is None or common.DEBUG_MODE:
+      if deterministic is not None and not common.DEBUG_MODE:
         warnings.warn("The `deterministic` argument has no effect unless the "
                       "`num_parallel_calls` argument is specified.")
       return BatchDataset(self, batch_size, drop_remainder, name=name)
@@ -1980,8 +1980,8 @@ def map(self,
     Returns:
       Dataset: A `Dataset`.
     """
-    if num_parallel_calls is None or DEBUG_MODE:
-      if deterministic is not None and not DEBUG_MODE:
+    if num_parallel_calls is None or common.DEBUG_MODE:
+      if deterministic is not None and not common.DEBUG_MODE:
         warnings.warn("The `deterministic` argument has no effect unless the "
                       "`num_parallel_calls` argument is specified.")
       return MapDataset(self, map_func, preserve_cardinality=True, name=name)
@@ -2144,8 +2144,8 @@ def interleave(
     if cycle_length is None:
       cycle_length = AUTOTUNE
 
-    if num_parallel_calls is None or DEBUG_MODE:
-      if deterministic is not None and not DEBUG_MODE:
+    if num_parallel_calls is None or common.DEBUG_MODE:
+      if deterministic is not None and not common.DEBUG_MODE:
         warnings.warn("The `deterministic` argument has no effect unless the "
                       "`num_parallel_calls` argument is specified.")
       return InterleaveDataset(
@@ -3795,7 +3795,7 @@ def map(self,
           num_parallel_calls=None,
           deterministic=None,
           name=None):
-    if num_parallel_calls is None or DEBUG_MODE:
+    if num_parallel_calls is None or common.DEBUG_MODE:
       return DatasetV1Adapter(
           MapDataset(self, map_func, preserve_cardinality=False))
     else:
@@ -6393,5 +6393,4 @@ def func(x):
 
 
 def toggle_debug_mode(debug_mode):
-  global DEBUG_MODE
-  DEBUG_MODE = debug_mode
+  common.DEBUG_MODE = debug_mode