class_docstr: A sensor applies a continuous sequence of numerical values
  to a set of associated metrics. For example a sensor on
  message size would record a sequence of message sizes using
  the `record(double)` api and would maintain a set
  of metrics about request sizes such as the average or max.
class_docstr_tok: ["A","sensor","applies","a","continuous","sequence","of","numerical","values","to","a","set","of","associated","metrics",".","For","example","a","sensor","on","message","size","would","record","a","sequence","of","message","sizes","using","the","`","record","(","double",")","`","api","and","would","maintain","a","set","of","metrics","about","request","sizes","such","as","the","average","or","max","."]
docstr: The name this sensor is registered with.
  This name will be unique among all registered sensors.
docstr_tok: ["The","name","this","sensor","is","registered","with",".","This","name","will","be","unique","among","all","registered","sensors","."]
code: def name(self):
      """
      The name this sensor is registered with.
      This name will be unique among all registered sensors.
      """
      return self._name
  
file_path: kafka-python/kafka/metrics/stats/sensor.py
imports: from __future__ import absolute_import
  import threading
  import time
  from kafka.errors import QuotaViolationError
  from kafka.metrics import KafkaMetric
label_desc: Use image node_id 3 for calling the Sensor obj's underlying member method code with example usage: obj.name() and returns: self