parameters: (cls, topic, partition=0, servers=None, configuration=None)
return_statement: return kafka_io_layer_ops.KafkaIOLayer(topic, partition, servers, configuration)
docstring: Obtain a KafkaIOLayer to be used with tf.keras.
  
          Args:
            topic: A `tf.string` tensor containing topic.
            partition: A `tf.int32` tensor containing partition.
            servers: A list of bootstrap servers.
            configurations: A `tf.string` tensor containing global configuration
              properties in [Key=Value] format,eg.
              ["enable.auto.commit=false", "heartbeat.interval.ms=2000"],
              please refer to 'Global configuration properties'
              in librdkafka doc.
  
          Returns:
            A class of `KafkaIOLayer`.
docstring_summary: Obtain a KafkaIOLayer to be used with tf.keras.
docstring_tokens: Obtain a KafkaIOLayer to be used with tf . keras .
function: def kafka(cls, topic, partition=0, servers=None, configuration=None):
          """Obtain a KafkaIOLayer to be used with tf.keras.
  
          Args:
            topic: A `tf.string` tensor containing topic.
            partition: A `tf.int32` tensor containing partition.
            servers: A list of bootstrap servers.
            configurations: A `tf.string` tensor containing global configuration
              properties in [Key=Value] format,eg.
              ["enable.auto.commit=false", "heartbeat.interval.ms=2000"],
              please refer to 'Global configuration properties'
              in librdkafka doc.
  
          Returns:
            A class of `KafkaIOLayer`.
          """
          return kafka_io_layer_ops.KafkaIOLayer(topic, partition, servers, configuration)
function_tokens: def kafka ( cls , topic , partition = 0 , servers = None , configuration = None ) : return kafka_io_layer_ops . KafkaIOLayer ( topic , partition , servers , configuration )