# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Experimental API for building input pipelines.

This module contains experimental `Dataset` sources and transformations that can
be used in conjunction with the `tf.data.Dataset` API. Note that the
`tf.data.experimental` API is not subject to the same backwards compatibility
guarantees as `tf.data`, but we will provide deprecation advice in advance of
removing existing functionality.

See [Importing Data](https://tensorflow.org/guide/datasets) for an overview.

@@Counter
@@CheckpointInputPipelineHook
@@CsvDataset
@@DatasetStructure
@@DistributeOptions
@@MapVectorizationOptions
@@OptimizationOptions
@@Optional
@@OptionalStructure
@@RaggedTensorStructure
@@RandomDataset
@@Reducer
@@SparseTensorStructure
@@SqlDataset
@@StatsAggregator
@@StatsOptions
@@Structure
@@TFRecordWriter
@@TensorArrayStructure
@@TensorStructure
@@ThreadingOptions

@@bucket_by_sequence_length
@@bytes_produced_stats
@@cardinality
@@choose_from_datasets
@@copy_to_device
@@dense_to_sparse_batch
@@enumerate_dataset
@@from_variant
@@get_next_as_optional
@@get_single_element
@@get_structure
@@group_by_reducer
@@group_by_window
@@ignore_errors
@@latency_stats
@@make_batched_features_dataset
@@make_csv_dataset
@@make_saveable_from_iterator
@@map_and_batch
@@map_and_batch_with_legacy_function
@@parallel_interleave
@@parse_example_dataset
@@prefetch_to_device
@@rejection_resample
@@sample_from_datasets
@@scan
@@shuffle_and_repeat
@@take_while
@@to_variant
@@unbatch
@@unique

@@AUTOTUNE
@@INFINITE_CARDINALITY
@@UNKNOWN_CARDINALITY

"""

from __future__ import print_function as _print_function

import sys as _sys

from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch
from tensorflow.python.data.experimental.ops.batching import map_and_batch
from tensorflow.python.data.experimental.ops.batching import map_and_batch_with_legacy_function
from tensorflow.python.data.experimental.ops.batching import unbatch
from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY
from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY
from tensorflow.python.data.experimental.ops.cardinality import cardinality
from tensorflow.python.data.experimental.ops.counter import Counter
from tensorflow.python.data.experimental.ops.distribute_options import DistributeOptions
from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset
from tensorflow.python.data.experimental.ops.error_ops import ignore_errors
from tensorflow.python.data.experimental.ops.get_single_element import get_single_element
from tensorflow.python.data.experimental.ops.grouping import Reducer
from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length
from tensorflow.python.data.experimental.ops.grouping import group_by_reducer
from tensorflow.python.data.experimental.ops.grouping import group_by_window
from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets
from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave
from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets
from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook
from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator
from tensorflow.python.data.experimental.ops.optimization_options import MapVectorizationOptions
from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions
from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset
from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device
from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device
from tensorflow.python.data.experimental.ops.random_ops import RandomDataset
from tensorflow.python.data.experimental.ops.readers import CsvDataset
from tensorflow.python.data.experimental.ops.readers import SqlDataset
from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset
from tensorflow.python.data.experimental.ops.readers import make_csv_dataset
from tensorflow.python.data.experimental.ops.resampling import rejection_resample
from tensorflow.python.data.experimental.ops.scan_ops import scan
from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat
from tensorflow.python.data.experimental.ops.stats_aggregator import StatsAggregator
from tensorflow.python.data.experimental.ops.stats_ops import bytes_produced_stats
from tensorflow.python.data.experimental.ops.stats_ops import latency_stats
from tensorflow.python.data.experimental.ops.stats_options import StatsOptions
from tensorflow.python.data.experimental.ops.take_while_ops import take_while
from tensorflow.python.data.experimental.ops.threading_options import ThreadingOptions
from tensorflow.python.data.experimental.ops.unique import unique
from tensorflow.python.data.experimental.ops.writers import TFRecordWriter
from tensorflow.python.data.ops.dataset_ops import AUTOTUNE
from tensorflow.python.data.ops.dataset_ops import DatasetSpec as DatasetStructure
from tensorflow.python.data.ops.dataset_ops import from_variant
from tensorflow.python.data.ops.dataset_ops import get_structure
from tensorflow.python.data.ops.dataset_ops import to_variant
from tensorflow.python.data.ops.iterator_ops import get_next_as_optional
from tensorflow.python.data.ops.optional_ops import Optional
from tensorflow.python.data.ops.optional_ops import OptionalSpec as OptionalStructure
from tensorflow.python.data.util.structure import _RaggedTensorStructure as RaggedTensorStructure
from tensorflow.python.data.util.structure import _SparseTensorStructure as SparseTensorStructure
from tensorflow.python.data.util.structure import _TensorArrayStructure as TensorArrayStructure
from tensorflow.python.data.util.structure import _TensorStructure as TensorStructure
from tensorflow.python.framework.type_spec import TypeSpec as Structure

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "compat.v1.data.experimental", public_apis=None, deprecation=False,
      has_lite=False)
