class_docstr: Computes the n_steps-step moving average of the time series, with
  the given relative weights assigned to each time in the moving average
  (default is to take the non-weighted average). Zero-pads the input time
  series to the left before taking the moving average.
class_docstr_tok: ["Computes","the","n_steps-step","moving","average","of","the","time","series",",","with","the","given","relative","weights","assigned","to","each","time","in","the","moving","average","(","default","is","to","take","the","non-weighted","average",")",".","Zero-pads","the","input","time","series","to","the","left","before","taking","the","moving","average","."]
code: def _n_pad(self):
      return len(self.weights) - 1
  
imports: from collections import OrderedDict
  import logging
  from typing import Sequence
  import numpy
  import scipy.signal
  from scipy.stats import norm
  from merlion.transform.base import TransformBase, InvertibleTransformBase
  from merlion.utils import UnivariateTimeSeries, TimeSeries
label_desc: Use image node_id 3 for calling the MovingAverage obj's underlying member method code with example usage: obj._n_pad() and returns: unknown