parameters: (self, topic, partition, offset, servers, configuration, internal=True)
docstring: Creates a `StreamIODataset` from kafka server with only a start offset.
  
          Args:
            topic: A `tf.string` tensor containing topic subscription.
            partition: A `tf.int64` tensor containing the partition.
            offset: A `tf.int64` tensor containing the start offset.
            servers: An optional list of bootstrap servers.
               For example: `localhost:9092`.
            configuration: An optional `tf.string` tensor containing
              configurations in [Key=Value] format.
              Global configuration: please refer to 'Global configuration properties'
                in librdkafka doc. Examples include
                ["enable.auto.commit=false", "heartbeat.interval.ms=2000"]
              Topic configuration: please refer to 'Topic configuration properties'
                in librdkafka doc. Note all topic configurations should be
                prefixed with `conf.topic.`. Examples include
                ["conf.topic.auto.offset.reset=earliest"]
              Reference: https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
            internal: Whether the dataset is being created from within the named scope.
              Default: True
docstring_summary: Creates a `StreamIODataset` from kafka server with only a start offset.
docstring_tokens: Creates a StreamIODataset from kafka server with only a start offset .
function: def __init__(self, topic, partition, offset, servers, configuration, internal=True):
          """Creates a `StreamIODataset` from kafka server with only a start offset.
  
          Args:
            topic: A `tf.string` tensor containing topic subscription.
            partition: A `tf.int64` tensor containing the partition.
            offset: A `tf.int64` tensor containing the start offset.
            servers: An optional list of bootstrap servers.
               For example: `localhost:9092`.
            configuration: An optional `tf.string` tensor containing
              configurations in [Key=Value] format.
              Global configuration: please refer to 'Global configuration properties'
                in librdkafka doc. Examples include
                ["enable.auto.commit=false", "heartbeat.interval.ms=2000"]
              Topic configuration: please refer to 'Topic configuration properties'
                in librdkafka doc. Note all topic configurations should be
                prefixed with `conf.topic.`. Examples include
                ["conf.topic.auto.offset.reset=earliest"]
              Reference: https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
            internal: Whether the dataset is being created from within the named scope.
              Default: True
          """
          with tf.name_scope("KafkaStreamIODataset"):
              assert internal
  
              metadata = list(configuration or [])
              if servers is not None:
                  metadata.append("bootstrap.servers=%s" % servers)
              resource = core_ops.io_kafka_readable_init(
                  topic, partition, offset, metadata=metadata
              )
  
              self._resource = resource
  
              dataset = tf.data.experimental.Counter()
              dataset = dataset.map(
                  lambda i: core_ops.io_kafka_readable_next(self._resource, i)
              )
              dataset = dataset.apply(
                  tf.data.experimental.take_while(
                      lambda v: tf.greater(tf.shape(v.message)[0], 0)
                  )
              )
              dataset = dataset.unbatch()
  
              self._dataset = dataset
              super().__init__(
                  self._dataset._variant_tensor
              )
function_tokens: def __init__ ( self , topic , partition , offset , servers , configuration , internal = True ) : with tf . name_scope ( "KafkaStreamIODataset" ) : assert internal metadata = list ( configuration or [ ] ) if servers is not None : metadata . append ( "bootstrap.servers=%s" % servers ) resource = core_ops . io_kafka_readable_init ( topic , partition , offset , metadata = metadata ) self . _resource = resource dataset = tf . data . experimental . Counter ( ) dataset = dataset . map ( lambda i : core_ops . io_kafka_readable_next ( self . _resource , i ) ) dataset = dataset . apply ( tf . data . experimental . take_while ( lambda v : tf . greater ( tf . shape ( v . message ) [ 0 ] , 0 ) ) ) dataset = dataset . unbatch ( ) self . _dataset = dataset super ( ) . __init__ ( self . _dataset . _variant_tensor )