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diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py
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+++ b/tensorflow/python/training/training_util.py
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+"""Utility functions for training."""
+import os.path
+
+from tensorflow.python.platform import gfile
+
+
+def global_step(sess, global_step_tensor):
+  """Small helper to get the global step.
+
+  ```python
+  # Creates a variable to hold the global_step.
+  global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
+  # Creates a session.
+  sess = tf.Session()
+  # Initializes the variable.
+  sess.run(global_step_tensor.initializer)
+  print 'global_step:', tf.train.global_step(sess, global_step_tensor)
+
+  global_step: 10
+  ```
+
+  Args:
+    sess: A brain `Session` object.
+    global_step_tensor:  `Tensor` or the `name` of the operation that contains
+      the global step.
+
+  Returns:
+    The global step value.
+  """
+  return int(sess.run(global_step_tensor))
+
+
+def write_graph(graph_def, logdir, name, as_text=True):
+  """Writes a graph proto on disk.
+
+  The graph is written as a binary proto unless as_text is `True`.
+
+  ```python
+  v = tf.Variable(0, name='my_variable')
+  sess = tf.Session()
+  tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
+  ```
+
+  Args:
+    graph_def: A `GraphDef` protocol buffer.
+    logdir: Directory where to write the graph.
+    name: Filename for the graph.
+    as_text: If `True`, writes the graph as an ASCII proto.
+  """
+  path = os.path.join(logdir, name)
+  gfile.MakeDirs(os.path.dirname(path))
+  f = gfile.FastGFile(path, "w")
+  if as_text:
+    f.write(str(graph_def))
+  else:
+    f.write(graph_def.SerializeToString())
+  f.close()