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","."]
code: def metrics(self):
      return tuple(self._metrics)
  
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 4 for calling the Sensor obj's underlying member method code with example usage: obj.metrics() and returns: tuple