old_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Kafka Dataset."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.kafka.python.ops import gen_kafka_ops
  from tensorflow.contrib.util import loader
  from tensorflow.python.data.ops.readers import Dataset
  from tensorflow.python.framework import common_shapes
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.platform import resource_loader
  
  class KafkaDataset(Dataset):
    """A Kafka Dataset that consumes the message.
    """
  
    def __init__(self, topics, servers="localhost", group="", eof=False, timeout=1000):
      """Create a KafkaReader.
  
      Args:
        topics: A `tf.string` tensor containing one or more subscriptions,
                in the format of [topic:partition:offset:length],
                by default length is -1 for unlimited.
        servers: A list of bootstrap servers.
        group: The consumer group id.
        eof: If True, the kafka reader will stop on EOF.
        timeout: The timeout value for the Kafka Consumer to wait
                 (in millisecond).
      """
      super(KafkaDataset, self).__init__()
      self._topics = ops.convert_to_tensor(
          topics, dtype=dtypes.string, name="topics")
      self._servers = ops.convert_to_tensor(
          servers, dtype=dtypes.string, name="servers")
      self._group = ops.convert_to_tensor(
          group, dtype=dtypes.string, name="group")
      self._eof = ops.convert_to_tensor(
          eof, dtype=dtypes.bool, name="eof")
      self._timeout = ops.convert_to_tensor(
          timeout, dtype=dtypes.int64, name="timeout")
  
    def _as_variant_tensor(self):
      return gen_kafka_ops.kafka_dataset(
          self._topics, self._servers, self._group, self._eof, self._timeout)
  
    @property
    def output_classes(self):
      return ops.Tensor
  
    @property
    def output_shapes(self):
      return tensor_shape.scalar()
  
    @property
    def output_types(self):
      return dtypes.string
  
new_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Kafka Dataset."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.kafka.python.ops import gen_kafka_ops
  from tensorflow.contrib.util import loader
  from tensorflow.python.data.ops.readers import Dataset
  from tensorflow.python.framework import common_shapes
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.platform import resource_loader
  
  class KafkaDataset(Dataset):
    """A Kafka Dataset that consumes the message.
    """
  
    def __init__(
        self, topics, servers="localhost", group="", eof=False, timeout=1000):
      """Create a KafkaReader.
  
      Args:
        topics: A `tf.string` tensor containing one or more subscriptions,
                in the format of [topic:partition:offset:length],
                by default length is -1 for unlimited.
        servers: A list of bootstrap servers.
        group: The consumer group id.
        eof: If True, the kafka reader will stop on EOF.
        timeout: The timeout value for the Kafka Consumer to wait
                 (in millisecond).
      """
      super(KafkaDataset, self).__init__()
      self._topics = ops.convert_to_tensor(
          topics, dtype=dtypes.string, name="topics")
      self._servers = ops.convert_to_tensor(
          servers, dtype=dtypes.string, name="servers")
      self._group = ops.convert_to_tensor(
          group, dtype=dtypes.string, name="group")
      self._eof = ops.convert_to_tensor(
          eof, dtype=dtypes.bool, name="eof")
      self._timeout = ops.convert_to_tensor(
          timeout, dtype=dtypes.int64, name="timeout")
  
    def _as_variant_tensor(self):
      return gen_kafka_ops.kafka_dataset(
          self._topics, self._servers, self._group, self._eof, self._timeout)
  
    @property
    def output_classes(self):
      return ops.Tensor
  
    @property
    def output_shapes(self):
      return tensor_shape.scalar()
  
    @property
    def output_types(self):
      return dtypes.string
  
subject: Address sanity build issues. (#16647)