# Path: qnd/estimator.py
# def def_estimator(distributed=False):
#     config = def_config(distributed)
# 
#     @util.func_scope
#     def estimator(model_fn, model_dir):
#         return tf.contrib.learn.Estimator(_wrap_model_fn(model_fn),
#                                           config=config(),
#                                           model_dir=model_dir)
# 
#     return estimator
# 
# Path: qnd/flag.py
# FLAGS = gargparse.ARGS
# 
# def add_output_dir_flag():
#     add_flag("output_dir",
#              default="output",
#              help="Directory where checkpoint and event files are stored")
# 
# Path: qnd/inputs.py
# DEFAULT_BATCH_SIZE = 64
# MODES = [tf.contrib.learn.ModeKeys.TRAIN,
#          tf.contrib.learn.ModeKeys.EVAL,
#          tf.contrib.learn.ModeKeys.INFER]
# def _add_file_flag(mode):
# def def_def_def_input_fn(mode):
#     def def_def_input_fn(batch_inputs=True, prepare_filename_queues=True):
#         def def_input_fn(user_input_fn):
#             def input_fn():
# def _batch_inputs(inputs, mode):
# def _batch_merged_inputs(inputs, mode):
# def _shuffle(inputs, capacity, min_after_dequeue, num_threads):
# def _merge_dicts(*dicts):
# def _check_inputs(inputs):
# def def_filenames_to_queue(mode):
#     def filenames_to_queue(filenames):


from .estimator import def_estimator
from .flag import FLAGS, add_output_dir_flag
from .inputs import def_def_infer_input_fn


def def_infer(batch_inputs=True, prepare_filename_queues=True):
    """Define `infer()` function.

    See also `help(def_infer())`.

    - Args
        - `batch_inputs`: Same as `def_train_and_evaluate()`'s.
        - `prepare_filename_queues`: Same as `def_train_and_evaluate()`'s.

    - Returns
        - `infer()` function.
    """
    add_output_dir_flag()


# Path: qnd/estimator.py
# def def_estimator(distributed=False):
#     config = def_config(distributed)
# 
#     @util.func_scope
#     def estimator(model_fn, model_dir):
#         return tf.contrib.learn.Estimator(_wrap_model_fn(model_fn),
#                                           config=config(),
#                                           model_dir=model_dir)
# 
#     return estimator
# 
# Path: qnd/flag.py
# FLAGS = gargparse.ARGS
# 
# def add_output_dir_flag():
#     add_flag("output_dir",
#              default="output",
#              help="Directory where checkpoint and event files are stored")
# 
# Path: qnd/inputs.py
# DEFAULT_BATCH_SIZE = 64
# MODES = [tf.contrib.learn.ModeKeys.TRAIN,
#          tf.contrib.learn.ModeKeys.EVAL,
#          tf.contrib.learn.ModeKeys.INFER]
# def _add_file_flag(mode):
# def def_def_def_input_fn(mode):
#     def def_def_input_fn(batch_inputs=True, prepare_filename_queues=True):
#         def def_input_fn(user_input_fn):
#             def input_fn():
# def _batch_inputs(inputs, mode):
# def _batch_merged_inputs(inputs, mode):
# def _shuffle(inputs, capacity, min_after_dequeue, num_threads):
# def _merge_dicts(*dicts):
# def _check_inputs(inputs):
# def def_filenames_to_queue(mode):
#     def filenames_to_queue(filenames):


# Path: qnd/infer.py
from .estimator import def_estimator
from .flag import FLAGS, add_output_dir_flag
from .inputs import def_def_infer_input_fn



def def_infer(batch_inputs=True, prepare_filename_queues=True):
    """Define `infer()` function.

    See also `help(def_infer())`.

    - Args
        - `batch_inputs`: Same as `def_train_and_evaluate()`'s.
        - `prepare_filename_queues`: Same as `def_train_and_evaluate()`'s.

    - Returns
        - `infer()` function.
    """
    add_output_dir_flag()


    estimator = def_estimator(distributed=False)