old_contents: # Copyright 2015 Google Inc. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """Utility functions for training."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  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: %s' % 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()
  
new_contents: # Copyright 2015 Google Inc. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """Utility functions for training."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  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: %s' % 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.
    """
    if not gfile.IsDirectory(logdir):
      gfile.MakeDirs(logdir)
    path = os.path.join(logdir, name)
    if as_text:
      f = gfile.FastGFile(path, "w")
      f.write(str(graph_def))
    else:
      f = gfile.FastGFile(path, "wb")
      f.write(graph_def.SerializeToString())
    f.close()
  
subject: Fix file exists error