# Path: tf_slim/data/data_provider.py
# class DataProvider(object):
#   def __init__(self, items_to_tensors, num_samples):
#   def get(self, items):
#   def list_items(self):
#   def num_samples(self):
#   def _validate_items(self, items):
# 
# Path: tf_slim/data/parallel_reader.py
# class ParallelReader(io_ops.ReaderBase):
#   def __init__(self,
#                reader_class,
#                common_queue,
#                num_readers=4,
#                reader_kwargs=None):
#   def num_readers(self):
#   def common_queue(self):
#   def read(self, queue, name=None):
#   def read_up_to(self, queue, num_records, name=None):
#   def _configure_readers_by(self, queue):
#   def num_records_produced(self, name=None):
#   def num_work_units_completed(self, name=None):
# def parallel_read(data_sources,
#                   reader_class,
#                   num_epochs=None,
#                   num_readers=4,
#                   reader_kwargs=None,
#                   shuffle=True,
#                   dtypes=None,
#                   capacity=256,
#                   min_after_dequeue=128,
#                   seed=None,
#                   scope=None):
# def single_pass_read(data_sources, reader_class, reader_kwargs=None,
#                      scope=None):
# def get_data_files(data_sources):


from tf_slim.data import data_provider
from tf_slim.data import parallel_reader
               shuffle=True,
               num_epochs=None,
               common_queue_capacity=256,
               common_queue_min=128,
               record_key='record_key',
               seed=None,
               scope=None):
    """Creates a DatasetDataProvider.

    Note: if `num_epochs` is not `None`,  local counter `epochs` will be created
    by relevant function. Use `local_variables_initializer()` to initialize
    local variables.
    Args:
      dataset: An instance of the Dataset class.
      num_readers: The number of parallel readers to use.
      reader_kwargs: An optional dict of kwargs for the reader.
      shuffle: Whether to shuffle the data sources and common queue when
        reading.
      num_epochs: The number of times each data source is read. If left as None,
        the data will be cycled through indefinitely.
      common_queue_capacity: The capacity of the common queue.
      common_queue_min: The minimum number of elements in the common queue after
        a dequeue.
      record_key: The item name to use for the dataset record keys in the
        provided tensors.
      seed: The seed to use if shuffling.
      scope: Optional name scope for the ops.
    Raises:
      ValueError: If `record_key` matches one of the items in the dataset.
    """

# Path: tf_slim/data/data_provider.py
# class DataProvider(object):
#   def __init__(self, items_to_tensors, num_samples):
#   def get(self, items):
#   def list_items(self):
#   def num_samples(self):
#   def _validate_items(self, items):
# 
# Path: tf_slim/data/parallel_reader.py
# class ParallelReader(io_ops.ReaderBase):
#   def __init__(self,
#                reader_class,
#                common_queue,
#                num_readers=4,
#                reader_kwargs=None):
#   def num_readers(self):
#   def common_queue(self):
#   def read(self, queue, name=None):
#   def read_up_to(self, queue, num_records, name=None):
#   def _configure_readers_by(self, queue):
#   def num_records_produced(self, name=None):
#   def num_work_units_completed(self, name=None):
# def parallel_read(data_sources,
#                   reader_class,
#                   num_epochs=None,
#                   num_readers=4,
#                   reader_kwargs=None,
#                   shuffle=True,
#                   dtypes=None,
#                   capacity=256,
#                   min_after_dequeue=128,
#                   seed=None,
#                   scope=None):
# def single_pass_read(data_sources, reader_class, reader_kwargs=None,
#                      scope=None):
# def get_data_files(data_sources):


# Path: tf_slim/data/dataset_data_provider.py
from tf_slim.data import data_provider
from tf_slim.data import parallel_reader

               shuffle=True,
               num_epochs=None,
               common_queue_capacity=256,
               common_queue_min=128,
               record_key='record_key',
               seed=None,
               scope=None):
    """Creates a DatasetDataProvider.

    Note: if `num_epochs` is not `None`,  local counter `epochs` will be created
    by relevant function. Use `local_variables_initializer()` to initialize
    local variables.
    Args:
      dataset: An instance of the Dataset class.
      num_readers: The number of parallel readers to use.
      reader_kwargs: An optional dict of kwargs for the reader.
      shuffle: Whether to shuffle the data sources and common queue when
        reading.
      num_epochs: The number of times each data source is read. If left as None,
        the data will be cycled through indefinitely.
      common_queue_capacity: The capacity of the common queue.
      common_queue_min: The minimum number of elements in the common queue after
        a dequeue.
      record_key: The item name to use for the dataset record keys in the
        provided tensors.
      seed: The seed to use if shuffling.
      scope: Optional name scope for the ops.
    Raises:
      ValueError: If `record_key` matches one of the items in the dataset.
    """

    key, data = parallel_reader.parallel_read(