import qnd
import tensorflow as tf


def model_fn(x, y):
    return (y,
            0.0,
            tf.contrib.framework.get_or_create_global_step().assign_add())


def input_fn(q):
    shape = (100,)
    return tf.zeros(shape, tf.float32), tf.ones(shape, tf.int32)


train_and_evaluate = qnd.def_train_and_evaluate()


def main():
    train_and_evaluate(model_fn, input_fn)


if __name__ == "__main__":
    main()

import qnd
import tensorflow as tf


def model_fn(x, y):
    return (y,
            0.0,
            tf.contrib.framework.get_or_create_global_step().assign_add())


def input_fn(q):
    shape = (100,)
    return tf.zeros(shape, tf.float32), tf.ones(shape, tf.int32)


train_and_evaluate = qnd.def_train_and_evaluate(distributed=True)


def main():
    train_and_evaluate(model_fn, input_fn)


if __name__ == "__main__":
    main()

Use distributed flag for xfail test
Use distributed flag for xfail test

  import qnd
  import tensorflow as tf
  
  
  def model_fn(x, y):
      return (y,
              0.0,
              tf.contrib.framework.get_or_create_global_step().assign_add())
  
  
  def input_fn(q):
      shape = (100,)
      return tf.zeros(shape, tf.float32), tf.ones(shape, tf.int32)
  
  
- train_and_evaluate = qnd.def_train_and_evaluate()
+ train_and_evaluate = qnd.def_train_and_evaluate(distributed=True)
  
  
  def main():
      train_and_evaluate(model_fn, input_fn)
  
  
  if __name__ == "__main__":
      main()
  
Use distributed flag for xfail test
## Code Before:
import qnd
import tensorflow as tf


def model_fn(x, y):
    return (y,
            0.0,
            tf.contrib.framework.get_or_create_global_step().assign_add())


def input_fn(q):
    shape = (100,)
    return tf.zeros(shape, tf.float32), tf.ones(shape, tf.int32)


train_and_evaluate = qnd.def_train_and_evaluate()


def main():
    train_and_evaluate(model_fn, input_fn)


if __name__ == "__main__":
    main()

## Instruction:
Use distributed flag for xfail test
## Code After:
import qnd
import tensorflow as tf


def model_fn(x, y):
    return (y,
            0.0,
            tf.contrib.framework.get_or_create_global_step().assign_add())


def input_fn(q):
    shape = (100,)
    return tf.zeros(shape, tf.float32), tf.ones(shape, tf.int32)


train_and_evaluate = qnd.def_train_and_evaluate(distributed=True)


def main():
    train_and_evaluate(model_fn, input_fn)


if __name__ == "__main__":
    main()
