class_docstr: Water flow through a pipeline branch.
  
  The series includes hourly values for about 2 months, March 2022 to May 2022. The values are
  expressed in liters per second. There are four anomalous segments in the series:
  
  * 3 "low value moments": this is due to water losses or human intervention for maintenance
  * A small peak in the water inflow after the first 2 segments: this is due to a pumping
      operation into the main pipeline, when more water pressure is needed
  
  This dataset is well suited for time series forecasting models, as well as anomaly detection
  methods. Ideally, the goal is to build a time series forecasting model that is robust to the
  anomalous segments.
  
  This data has been kindly donated by the Tecnojest s.r.l. company (www.invidea.it) from Italy.
code: def __iter__(self):
      return stream.iter_csv(
          self.path,
          target="Water flow [l/s]",
          converters={"Water flow [l/s]": float},
          parse_dates={"Time": "%Y-%m-%dT%H:%M:%S%z"},
      )
  
imports: from __future__ import annotations
  from river import stream
  from .None import base
label_desc: Use image node_id 2 for calling the WaterFlow obj's underlying member method code with example usage: obj.__iter__() and returns: stream