class_docstr: Tracks the size and number of events in buckets.
  
  Histograms allow you to calculate aggregate quantiles
  such as 25, 50, 95, 99 percentile latency for an RPC.
  
  This corresponds to Prometheus' histogram metric:
  https://prometheus.io/docs/concepts/metric_types/#histogram
  
  Args:
      name: Name of the metric.
      description: Description of the metric.
      boundaries: Boundaries of histogram buckets.
      tag_keys: Tag keys of the metric.
class_docstr_tok: ["Tracks","the","size","and","number","of","events","in","buckets",".","Histograms","allow","you","to","calculate","aggregate","quantiles","such","as","25",",","50",",","95",",","99","percentile","latency","for","an","RPC",".","This","corresponds","to","Prometheus","'","histogram","metric",":","https",":","\/\/prometheus.io\/docs\/concepts\/metric_types\/","#","histogram","Args",":","name",":","Name","of","the","metric",".","description",":","Description","of","the","metric",".","boundaries",":","Boundaries","of","histogram","buckets",".","tag_keys",":","Tag","keys","of","the","metric","."]
code: def __reduce__(self):
      deserializer = Histogram
      serialized_data = (
          self._name,
          self._description,
          self.boundaries,
          self._tag_keys,
      )
      return deserializer, serialized_data
  
imports: import logging
  from typing import Dict, Any, List, Optional, Tuple, Union
  from ray._raylet import Sum, Histogram, Gauge
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 3 for calling the Histogram obj's underlying member method code with example usage: obj.__reduce__() and returns: deserializer,  serialized_data