def _make_parallel_scan_dataset(self, ds, num_parallel_scans,
                                  normalized_probability, normalized_columns):
    """Builds a parallel dataset from a given range.

    Args:
      ds: A `_BigtableSampleKeyPairsDataset` returning ranges of keys to use.
      num_parallel_scans: The number of concurrent parallel scans to use.
      normalized_probability: A number between 0 and 1 for the keep probability.
      normalized_columns: The column families and column qualifiers to retrieve.

    Returns:
      A @{tf.data.Dataset} representing the result of the parallel scan.
    """
    if num_parallel_scans is None:
      num_parallel_scans = 50

    ds = ds.shuffle(buffer_size=10000)  # TODO(saeta): Make configurable.

    def _interleave_fn(start, end):
      return _BigtableScanDataset(
          self,
          prefix="",
          start=start,
          end=end,
          normalized=normalized_columns,
          probability=normalized_probability)

    # Note prefetch_input_elements must be set in order to avoid rpc timeouts.
    ds = ds.apply(
        interleave_ops.parallel_interleave(
            _interleave_fn,
            cycle_length=num_parallel_scans,
            sloppy=True,
            prefetch_input_elements=1))
    return ds

  def testShutdownRace(self):
    dataset = dataset_ops.Dataset.range(20)
    map_fn = lambda x: dataset_ops.Dataset.range(20 * x, 20 * (x + 1))
    dataset = dataset.apply(
        interleave_ops.parallel_interleave(
            map_fn,
            cycle_length=3,
            sloppy=False,
            buffer_output_elements=1,
            prefetch_input_elements=0))
    dataset = dataset.batch(32)
    iterator = dataset.make_initializable_iterator()
    next_element = iterator.get_next()

    results = []
    with self.cached_session() as sess:
      for _ in range(2):
        elements = []
        sess.run(iterator.initializer)
        try:
          while True:
            elements.extend(sess.run(next_element))
        except errors.OutOfRangeError:
          pass
        results.append(elements)

    self.assertAllEqual(results[0], results[1])

  def testParallelInterleave(self):
    dataset = dataset_ops.Dataset.from_tensor_slices(
        self._createTFRecordFiles())
    dataset = dataset.apply(interleave_ops.parallel_interleave(
        readers.TFRecordDataset,
        cycle_length=4,
        block_length=self._num_records))
    dataset = input_ops.auto_shard_dataset(
        dataset, self._num_shards, self._shard_index)

    # Since block_length == num records in each file, the output will still
    # contain records in order of files.
    self._verifySimpleShardingOutput(dataset, self._record)

  def testErrorsInInputFn(self):

    def map_py_fn(x):
      if x == 5:
        raise ValueError()
      return x

    def map_fn(x):
      return script_ops.py_func(map_py_fn, [x], x.dtype)

    def interleave_fn(x):
      dataset = dataset_ops.Dataset.from_tensors(x)
      dataset = dataset.repeat(x)
      return dataset

    self.dataset = (
        dataset_ops.Dataset.from_tensor_slices(self.input_values).map(map_fn)
        .repeat(self.repeat_count).apply(
            interleave_ops.parallel_interleave(interleave_fn, self.cycle_length,
                                               self.block_length, self.sloppy,
                                               self.buffer_output_elements,
                                               self.prefetch_input_elements)))

    self.iterator = self.dataset.make_initializable_iterator()
    self.init_op = self.iterator.initializer
    self.next_element = self.iterator.get_next()

    with self.test_session() as sess:
      sess.run(
          self.init_op,
          feed_dict={
              self.input_values: [4, 5, 6],
              self.cycle_length: 2,
              self.block_length: 1,
              self.sloppy: False,
              self.buffer_output_elements: 1,
              self.prefetch_input_elements: 0,
          })
      for i, expected_element in enumerate(
          self._interleave([[4] * 4, [5], [6] * 6] * self.repeat_count, 2, 1)):
        if expected_element == 5:
          with self.assertRaises(errors.InvalidArgumentError):
            sess.run(self.next_element)
        else:
          actual_element = sess.run(self.next_element)
          self.assertEqual(expected_element, actual_element,
                           "At index %s: %s expected, got: %s" %
                           (i, expected_element, actual_element))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(self.next_element)

  def setUp(self):

    self.input_values = array_ops.placeholder(dtypes.int64, shape=[None])
    self.cycle_length = array_ops.placeholder(dtypes.int64, shape=[])
    self.block_length = array_ops.placeholder(dtypes.int64, shape=[])
    self.sloppy = array_ops.placeholder(dtypes.bool, shape=[])
    self.buffer_output_elements = array_ops.placeholder(dtypes.int64, shape=[])
    self.prefetch_input_elements = array_ops.placeholder(dtypes.int64, shape=[])

    self.error = None
    self.repeat_count = 2

    # Set up threading events used to sequence when items are produced that
    # are subsequently interleaved. These events allow us to deterministically
    # simulate slowdowns and force sloppiness.
    self.read_coordination_events = {}
    self.write_coordination_events = {}
    # input values [4, 5, 6] are the common case for the tests; set defaults
    for i in range(4, 7):
      self.read_coordination_events[i] = threading.Semaphore(0)
      self.write_coordination_events[i] = threading.Event()

    def map_py_fn(x):
      self.write_coordination_events[x].wait()
      self.write_coordination_events[x].clear()
      self.read_coordination_events[x].release()
      if self.error:
        err = self.error
        self.error = None
        raise err  # pylint: disable=raising-bad-type
      return x * x

    def map_fn(x):
      return script_ops.py_func(map_py_fn, [x], x.dtype)

    def interleave_fn(x):
      dataset = dataset_ops.Dataset.from_tensors(x)
      dataset = dataset.repeat(x)
      return dataset.map(map_fn)

    self.dataset = (
        dataset_ops.Dataset.from_tensor_slices(self.input_values)
        .repeat(self.repeat_count).apply(
            interleave_ops.parallel_interleave(interleave_fn, self.cycle_length,
                                               self.block_length, self.sloppy,
                                               self.buffer_output_elements,
                                               self.prefetch_input_elements)))
    self.iterator = self.dataset.make_initializable_iterator()
    self.init_op = self.iterator.initializer
    self.next_element = self.iterator.get_next()

  def _testTooManyReaders(self, sloppy=False):

    def interleave_fn(x):
      dataset = dataset_ops.Dataset.from_tensors(x)
      dataset = dataset.repeat(math_ops.cast(x, dtype=dtypes.int64))
      return dataset

    dataset = dataset_ops.Dataset.from_tensor_slices([4, 5, 6])
    dataset = dataset.repeat(self.repeat_count)
    dataset = dataset.apply(
        interleave_ops.parallel_interleave(
            interleave_fn, cycle_length=16, block_length=2, sloppy=sloppy))
    iterator = dataset.make_one_shot_iterator()

    with self.test_session() as sess:
      output_values = []
      for _ in range(30):
        output_values.append(sess.run(iterator.get_next()))

    expected_values = self._interleave(
        [[4] * 4, [5] * 5, [6] * 6] * self.repeat_count, 1, 2)
    self.assertItemsEqual(output_values, expected_values)

  def testSparse(self):
    def _map_fn(i):
      return sparse_tensor.SparseTensor(
          indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2])

    def _interleave_fn(x):
      return dataset_ops.Dataset.from_tensor_slices(
          sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values))

    dataset = dataset_ops.Dataset.range(10).map(_map_fn)
    iterator = dataset.apply(
        interleave_ops.parallel_interleave(
            _interleave_fn, cycle_length=1)).make_initializable_iterator()
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.test_session() as sess:
      sess.run(init_op)
      for i in range(10):
        for j in range(2):
          expected = [i, 0] if j % 2 == 0 else [0, -i]
          self.assertAllEqual(expected, sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

#.........这里部分代码省略.........

  ```
  features: {
    "age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
    "gender": FixedLenFeature([], dtype=tf.string),
    "kws": VarLenFeature(dtype=tf.string),
  }
  ```

  And the expected output is:

  ```python
  {
    "age": [[0], [-1]],
    "gender": [["f"], ["f"]],
    "kws": SparseTensor(
      indices=[[0, 0], [0, 1], [1, 0]],
      values=["code", "art", "sports"]
      dense_shape=[2, 2]),
  }
  ```

  Args:
    file_pattern: List of files or patterns of file paths containing
      `Example` records. See `tf.gfile.Glob` for pattern rules.
    batch_size: An int representing the number of records to combine
      in a single batch.
    features: A `dict` mapping feature keys to `FixedLenFeature` or
      `VarLenFeature` values. See `tf.parse_example`.
    reader: A function or class that can be
      called with a `filenames` tensor and (optional) `reader_args` and returns
      a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`.
    reader_args: Additional arguments to pass to the reader class.
    num_epochs: Integer specifying the number of times to read through the
      dataset. If None, cycles through the dataset forever. Defaults to `None`.
    shuffle: A boolean, indicates whether the input should be shuffled. Defaults
      to `True`.
    shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity
      ensures better shuffling but would increase memory usage and startup time.
    shuffle_seed: Randomization seed to use for shuffling.
    prefetch_buffer_size: Number of feature batches to prefetch in order to
      improve performance. Recommended value is the number of batches consumed
      per training step (default is 1).
    reader_num_threads: Number of threads used to read `Example` records. If >1,
      the results will be interleaved.
    parser_num_threads: Number of threads to use for parsing `Example` tensors
      into a dictionary of `Feature` tensors.
    sloppy_ordering: If `True`, reading performance will be improved at
      the cost of non-deterministic ordering. If `False`, the order of elements
      produced is deterministic prior to shuffling (elements are still
      randomized if `shuffle=True`. Note that if the seed is set, then order
      of elements after shuffling is deterministic). Defaults to `False`.
    drop_final_batch: If `True`, and the batch size does not evenly divide the
      input dataset size, the final smaller batch will be dropped. Defaults to
      `False`.

  Returns:
    A dataset of `dict` elements. Each `dict` maps feature keys to
    `Tensor` or `SparseTensor` objects.
  """
  # Create dataset of all matching filenames
  filenames = _get_file_names(file_pattern, False)
  dataset = dataset_ops.Dataset.from_tensor_slices(filenames)
  if shuffle:
    dataset = dataset.shuffle(len(filenames), shuffle_seed)

  # Read `Example` records from files as tensor objects.
  if reader_args is None:
    reader_args = []

  # Read files sequentially (if reader_num_threads=1) or in parallel
  dataset = dataset.apply(
      interleave_ops.parallel_interleave(
          lambda filename: reader(filename, *reader_args),
          cycle_length=reader_num_threads,
          sloppy=sloppy_ordering))

  # Extract values if the `Example` tensors are stored as key-value tuples.
  if dataset.output_types == (dtypes.string, dtypes.string):
    dataset = dataset.map(lambda _, v: v)

  # Apply dataset repeat and shuffle transformations.
  dataset = _maybe_shuffle_and_repeat(
      dataset, num_epochs, shuffle, shuffle_buffer_size, shuffle_seed)

  if drop_final_batch:
    dataset = dataset.apply(batching.batch_and_drop_remainder(batch_size))
  else:
    dataset = dataset.batch(batch_size)

  # Parse `Example` tensors to a dictionary of `Feature` tensors.
  dataset = dataset.map(
      lambda x: parsing_ops.parse_example(x, features),
      num_parallel_calls=parser_num_threads)

  # TODO(rachelim): Add an optional label_name argument for extracting the label
  # from the features dictionary, to comply with the type expected by the
  # input_fn to a `tf.Estimator.train` or `tf.Estimator.evaluate` function.
  dataset = dataset.prefetch(prefetch_buffer_size)
  return dataset

#.........这里部分代码省略.........
      randomized if `shuffle=True`. Note that if the seed is set, then order
      of elements after shuffling is deterministic). Defaults to `False`.
    num_rows_for_inference: Number of rows of a file to use for type inference
      if record_defaults is not provided. If None, reads all the rows of all
      the files. Defaults to 100.

  Returns:
    A dataset, where each element is a (features, labels) tuple that corresponds
    to a batch of `batch_size` CSV rows. The features dictionary maps feature
    column names to `Tensor`s containing the corresponding column data, and
    labels is a `Tensor` containing the column data for the label column
    specified by `label_name`.

  Raises:
    ValueError: If any of the arguments is malformed.
  """
  # Create dataset of all matching filenames
  filenames = _get_file_names(file_pattern, False)
  dataset = dataset_ops.Dataset.from_tensor_slices(filenames)
  if shuffle:
    dataset = dataset.shuffle(len(filenames), shuffle_seed)

  # Clean arguments; figure out column names and defaults

  if column_names is None:
    if not header:
      raise ValueError("Cannot infer column names without a header line.")
    # If column names are not provided, infer from the header lines
    column_names = _infer_column_names(filenames, field_delim, use_quote_delim)
  if len(column_names) != len(set(column_names)):
    raise ValueError("Cannot have duplicate column names.")

  if select_columns is not None:
    select_columns = _get_sorted_col_indices(select_columns, column_names)

  if column_defaults is not None:
    column_defaults = [
        constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x
        for x in column_defaults
    ]
  else:
    # If column defaults are not provided, infer from records at graph
    # construction time
    column_defaults = _infer_column_defaults(
        filenames, len(column_names), field_delim, use_quote_delim, na_value,
        header, num_rows_for_inference, select_columns)

  if select_columns is not None and len(column_defaults) != len(select_columns):
    raise ValueError(
        "If specified, column_defaults and select_columns must have same "
        "length."
    )
  if select_columns is not None and len(column_names) > len(select_columns):
    # Pick the relevant subset of column names
    column_names = [column_names[i] for i in select_columns]

  if label_name is not None and label_name not in column_names:
    raise ValueError("`label_name` provided must be one of the columns.")

  def filename_to_dataset(filename):
    return CsvDataset(
        filename,
        record_defaults=column_defaults,
        field_delim=field_delim,
        use_quote_delim=use_quote_delim,
        na_value=na_value,
        select_cols=select_columns,
        header=header)

  def map_fn(*columns):
    """Organizes columns into a features dictionary.

    Args:
      *columns: list of `Tensor`s corresponding to one csv record.
    Returns:
      An OrderedDict of feature names to values for that particular record. If
      label_name is provided, extracts the label feature to be returned as the
      second element of the tuple.
    """
    features = collections.OrderedDict(zip(column_names, columns))
    if label_name is not None:
      label = features.pop(label_name)
      return features, label
    return features

  # Read files sequentially (if num_parallel_reads=1) or in parallel
  dataset = dataset.apply(
      interleave_ops.parallel_interleave(
          filename_to_dataset, cycle_length=num_parallel_reads, sloppy=sloppy))

  dataset = _maybe_shuffle_and_repeat(
      dataset, num_epochs, shuffle, shuffle_buffer_size, shuffle_seed)

  # Apply batch before map for perf, because map has high overhead relative
  # to the size of the computation in each map
  dataset = dataset.batch(batch_size=batch_size)
  dataset = dataset.map(map_fn, num_parallel_calls=num_parallel_parser_calls)
  dataset = dataset.prefetch(prefetch_buffer_size)

  return dataset

#.........这里部分代码省略.........
    specified by `label_name`.

  Raises:
    ValueError: If any of the arguments is malformed.
  """
  # Create dataset of all matching filenames
  filenames = _get_file_names(file_pattern, False)
  dataset = dataset_ops.Dataset.from_tensor_slices(filenames)
  if shuffle:
    dataset = dataset.shuffle(len(filenames), shuffle_seed)

  # Clean arguments; figure out column names and defaults
  if comment is not None and len(comment) != 1:
    raise ValueError("`comment` arg must be a single-character string or None")

  if column_names is None:
    if not header:
      raise ValueError("Cannot infer column names without a header line.")
    # If column names are not provided, infer from the header lines
    column_names = _infer_column_names(filenames, field_delim, use_quote_delim)
  if len(column_names) != len(set(column_names)):
    raise ValueError("Cannot have duplicate column names.")

  if column_defaults is not None:
    column_defaults = [
        constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x
        for x in column_defaults
    ]
  else:
    # If column defaults are not provided, infer from records at graph
    # construction time
    column_defaults = _infer_column_defaults(
        filenames, len(column_names), field_delim, use_quote_delim, na_value,
        header, comment, default_float_type, num_rows_for_inference)

  if label_name is not None and label_name not in column_names:
    raise ValueError("`label_name` provided must be one of the columns.")

  # Define map and filter functions
  def filter_fn(line):
    return math_ops.not_equal(string_ops.substr(line, 0, 1), comment)

  def filename_to_dataset(filename):
    ds = core_readers.TextLineDataset(filename)
    if header:
      ds = ds.skip(1)
    if comment is not None:
      ds = ds.filter(filter_fn)
    return ds

  def decode_csv(line):
    """Decodes CSV line into features.

    Args:
      line: String tensor corresponding to one csv record.
    Returns:
      A dictionary of feature names to values for that particular record. If
      label_name is provided, extracts the label feature to be returned as the
      second element of the tuple.
    """
    columns = parsing_ops.decode_csv(
        line,
        column_defaults,
        field_delim=field_delim,
        use_quote_delim=use_quote_delim,
        na_value=na_value,
    )
    features = dict(zip(column_names, columns))
    if label_name is not None:
      label = features.pop(label_name)
      return features, label
    return features

  # Read files sequentially or in parallel
  dataset = dataset.apply(
      interleave_ops.parallel_interleave(
          filename_to_dataset, cycle_length=num_parallel_reads, sloppy=sloppy))

  if num_epochs != 1 and shuffle:
    # Use shuffle_and_repeat for perf
    dataset = dataset.apply(
        shuffle_ops.shuffle_and_repeat(shuffle_buffer_size, num_epochs,
                                       shuffle_seed))
  elif shuffle:
    dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed)
  elif num_epochs != 1:
    dataset = dataset.repeat(num_epochs)

  # Use map_and_batch for perf
  # TODO(b/76425672): use num_parallel_calls for better performance tuning when
  # that is added
  dataset = dataset.apply(
      batching.map_and_batch(
          map_func=decode_csv,
          batch_size=batch_size,
          num_parallel_batches=int(
              ceil(num_parallel_parser_calls / batch_size))))

  dataset = dataset.prefetch(prefetch_buffer_size)
  return dataset

#.........这里部分代码省略.........
    "age": [[0], [-1]],
    "gender": [["f"], ["f"]],
    "kws": SparseTensor(
      indices=[[0, 0], [0, 1], [1, 0]],
      values=["code", "art", "sports"]
      dense_shape=[2, 2]),
  }
  ```

  Args:
    file_pattern: List of files or patterns of file paths containing
      `Example` records. See `tf.gfile.Glob` for pattern rules.
    batch_size: An int representing the number of records to combine
      in a single batch.
    features: A `dict` mapping feature keys to `FixedLenFeature` or
      `VarLenFeature` values. See `tf.parse_example`.
    reader: A function or class that can be
      called with a `filenames` tensor and (optional) `reader_args` and returns
      a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`.
    label_key: (Optional) A string corresponding to the key labels are stored in
      `tf.Examples`. If provided, it must be one of the `features` key,
      otherwise results in `ValueError`.
    reader_args: Additional arguments to pass to the reader class.
    num_epochs: Integer specifying the number of times to read through the
      dataset. If None, cycles through the dataset forever. Defaults to `None`.
    shuffle: A boolean, indicates whether the input should be shuffled. Defaults
      to `True`.
    shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity
      ensures better shuffling but would increase memory usage and startup time.
    shuffle_seed: Randomization seed to use for shuffling.
    prefetch_buffer_size: Number of feature batches to prefetch in order to
      improve performance. Recommended value is the number of batches consumed
      per training step. Defaults to auto-tune.
    reader_num_threads: Number of threads used to read `Example` records. If >1,
      the results will be interleaved.
    parser_num_threads: Number of threads to use for parsing `Example` tensors
      into a dictionary of `Feature` tensors.
    sloppy_ordering: If `True`, reading performance will be improved at
      the cost of non-deterministic ordering. If `False`, the order of elements
      produced is deterministic prior to shuffling (elements are still
      randomized if `shuffle=True`. Note that if the seed is set, then order
      of elements after shuffling is deterministic). Defaults to `False`.
    drop_final_batch: If `True`, and the batch size does not evenly divide the
      input dataset size, the final smaller batch will be dropped. Defaults to
      `False`.

  Returns:
    A dataset of `dict` elements, (or a tuple of `dict` elements and label).
    Each `dict` maps feature keys to `Tensor` or `SparseTensor` objects.

  Raises:
    ValueError: If `label_key` is not one of the `features` keys.
  """
  # Create dataset of all matching filenames
  filenames = _get_file_names(file_pattern, False)
  dataset = dataset_ops.Dataset.from_tensor_slices(filenames)
  if shuffle:
    dataset = dataset.shuffle(len(filenames), shuffle_seed)

  # Read `Example` records from files as tensor objects.
  if reader_args is None:
    reader_args = []

  # Read files sequentially (if reader_num_threads=1) or in parallel
  dataset = dataset.apply(
      interleave_ops.parallel_interleave(
          lambda filename: reader(filename, *reader_args),
          cycle_length=reader_num_threads,
          sloppy=sloppy_ordering))

  # Extract values if the `Example` tensors are stored as key-value tuples.
  if dataset.output_types == (dtypes.string, dtypes.string):
    dataset = dataset_ops.MapDataset(
        dataset, lambda _, v: v, use_inter_op_parallelism=False)

  # Apply dataset repeat and shuffle transformations.
  dataset = _maybe_shuffle_and_repeat(
      dataset, num_epochs, shuffle, shuffle_buffer_size, shuffle_seed)

  # NOTE(mrry): We set `drop_remainder=True` when `num_epochs is None` to
  # improve the shape inference, because it makes the batch dimension static.
  # It is safe to do this because in that case we are repeating the input
  # indefinitely, and all batches will be full-sized.
  dataset = dataset.batch(
      batch_size, drop_remainder=drop_final_batch or num_epochs is None)

  # Parse `Example` tensors to a dictionary of `Feature` tensors.
  dataset = dataset.apply(
      parsing_ops.parse_example_dataset(
          features, num_parallel_calls=parser_num_threads))

  if label_key:
    if label_key not in features:
      raise ValueError(
          "The `label_key` provided (%r) must be one of the `features` keys." %
          label_key)
    dataset = dataset.map(lambda x: (x, x.pop(label_key)))

  dataset = dataset.prefetch(prefetch_buffer_size)
  return dataset

 def _build_dataset():
   return dataset_ops.Dataset.range(10).map(_map_fn).apply(
       interleave_ops.parallel_interleave(_interleave_fn, 1))

 def _build_ds(self, cycle_length, block_length, sloppy=False):
   return (dataset_ops.Dataset.from_tensor_slices(
       self.input_values).repeat(self.num_repeats).apply(
           interleave_ops.parallel_interleave(
               lambda x: dataset_ops.Dataset.range(10 * x, 11 * x),
               cycle_length, block_length, sloppy)))

#.........这里部分代码省略.........
      generated. By default, it will repeat infinitely.
    filename_shuffle_buffer_size: An optional integer whose value controls the
      shuffling of the file names. If you would like to read from the files in
      the same order, set to 0 or False.
    num_parallel_reads: An optional integer controlling the number of files to
      read from concurrently. (Set to 1 for no parallelism.)
    batch_transfer_size: An optional integer controlling the batching used to
      amortize the remote function invocation overhead. Set to a very large
      number to increase throughput. Set to a very small number to reduce memory
      consumption. Set to False to skip batching.
    sloppy: (Optional.) If `False`, read input data while maintaining a
      deterministic order. (This may have significant performance impacts.)
      sloppy defaults to: True.
  Returns:
    A `tf.data.Dataset` with an infinite stream of elements generated by a
    parallel interleaving of the set of files matched (or generated) by `files`
    with a type is the output of the dataset specified by `filetype`.

  Raises:
    ValueError: if any argument is not of the expected type.
  """
  if filetype is None:
    filetype = 'tfrecord'

  if isinstance(filetype, str):
    if filetype not in _FILETYPE_MAP:
      raise ValueError('Unexpected filetype: %s' % filetype)
    reader_fn = _FILETYPE_MAP[filetype]
  elif callable(filetype):
    reader_fn = filetype
  else:
    raise ValueError('filetype should be a string or a callable')

  file_reader_job = file_reader_job or 'coordinator'

  worker_job = worker_job or 'worker'

  if filename_shuffle_buffer_size is None:
    filename_shuffle_buffer_size = 4096

  num_parallel_reads = num_parallel_reads or 8

  if batch_transfer_size is None:
    batch_transfer_size = 256

  if sloppy is None:
    sloppy = True

  with ops.device('/job:%s' % file_reader_job):
    if isinstance(files, str):
      source_dataset = dataset_ops.Dataset.list_files(files)
    elif isinstance(files, dataset_ops.Dataset):
      source_dataset = files
    else:
      raise ValueError('files was not a string or a dataset: %s' % files)

    if filename_shuffle_buffer_size:
      source_dataset = source_dataset.shuffle(
          buffer_size=filename_shuffle_buffer_size)

    # NOTE: We perform the `repeat` on the source dataset, because the output
    # dataset does not currently have enough information to recreate an iterator
    # over the source dataset when it reaches the end.
    source_dataset = source_dataset.repeat(num_epochs)

    source_dataset = source_dataset.apply(
        interleave_ops.parallel_interleave(
            reader_fn, cycle_length=num_parallel_reads, sloppy=sloppy))

    if batch_transfer_size:
      source_dataset = source_dataset.batch(batch_transfer_size)

    source_dataset = source_dataset.prefetch(1)

    source_iterator = source_dataset.make_one_shot_iterator()
    source_handle = source_iterator.string_handle()

  @function.Defun(dtypes.string)
  def LoadingFunc(h):
    remote_iterator = iterator_ops.Iterator.from_string_handle(
        h, source_dataset.output_types, source_dataset.output_shapes)
    return remote_iterator.get_next()

  def MapFn(unused_input):
    return functional_ops.remote_call(
        args=[source_handle],
        Tout=[dtypes.string],
        f=LoadingFunc,
        target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)[0]

  with ops.device('/job:%s' % worker_job):
    output_dataset = dataset_ops.Dataset.range(2).repeat().map(
        MapFn, num_parallel_calls=4 if sloppy else None)
    output_dataset = output_dataset.prefetch(1)

    if batch_transfer_size:
      # Undo the batching used during the transfer.
      output_dataset = output_dataset.apply(batching.unbatch()).prefetch(1)

  return output_dataset

    def minibatch(self, dataset, subset, use_datasets, cache_data,
                  shift_ratio=-1):
        if shift_ratio < 0:
            shift_ratio = self.shift_ratio
        with tf.name_scope('batch_processing'):
            # Build final results per split.
            images = [[] for _ in range(self.num_splits)]
            labels = [[] for _ in range(self.num_splits)]
            if use_datasets:
                glob_pattern = dataset.tf_record_pattern(subset)
                file_names = gfile.Glob(glob_pattern)
                if not file_names:
                    raise ValueError('Found no files in --data_dir matching: {}'
                                     .format(glob_pattern))
                ds = tf.data.TFRecordDataset.list_files(file_names)
                ds = ds.apply(
                    interleave_ops.parallel_interleave(
                        tf.data.TFRecordDataset, cycle_length=10))
                if cache_data:
                    ds = ds.take(1).cache().repeat()
                counter = tf.data.Dataset.range(self.batch_size)
                counter = counter.repeat()
                ds = tf.data.Dataset.zip((ds, counter))
                ds = ds.prefetch(buffer_size=self.batch_size)
                ds = ds.shuffle(buffer_size=10000)
                ds = ds.repeat()
                ds = ds.apply(
                    batching.map_and_batch(
                        map_func=self.parse_and_preprocess,
                        batch_size=self.batch_size_per_split,
                        num_parallel_batches=self.num_splits))
                ds = ds.prefetch(buffer_size=self.num_splits)
                ds_iterator = ds.make_one_shot_iterator()
                for d in xrange(self.num_splits):
                    labels[d], images[d] = ds_iterator.get_next()

            else:
                record_input = data_flow_ops.RecordInput(
                    file_pattern=dataset.tf_record_pattern(subset),
                    seed=301,
                    parallelism=64,
                    buffer_size=10000,
                    batch_size=self.batch_size,
                    shift_ratio=shift_ratio,
                    name='record_input')
                records = record_input.get_yield_op()
                records = tf.split(records, self.batch_size, 0)
                records = [tf.reshape(record, []) for record in records]
                for idx in xrange(self.batch_size):
                    value = records[idx]
                    (label, image) = self.parse_and_preprocess(value, idx)
                    split_index = idx % self.num_splits
                    labels[split_index].append(label)
                    images[split_index].append(image)

            for split_index in xrange(self.num_splits):
                if not use_datasets:
                    images[split_index] = tf.parallel_stack(images[split_index])
                    labels[split_index] = tf.concat(labels[split_index], 0)
                images[split_index] = tf.cast(images[split_index], self.dtype)
                depth = 3
                images[split_index] = tf.reshape(
                    images[split_index],
                    shape=[self.batch_size_per_split, self.height, self.width, depth])
                labels[split_index] = tf.reshape(labels[split_index],
                                                 [self.batch_size_per_split])
            return images, labels