class_docstr: An InstanceSampler is called with the time series ``ts``, and returns a set
  of indices at which training instances will be generated.
  
  The sampled indices ``i`` satisfy ``a <= i <= b``, where ``a = min_past``
  and ``b = ts.shape[axis] - min_future``.
class_docstr_tok: ["An","InstanceSampler","is","called","with","the","time","series","``","ts","``",",","and","returns","a","set","of","indices","at","which","training","instances","will","be","generated",".","The","sampled","indices","``","i","``","satisfy","``","a","<","=","i","<","=","b","``",",","where","``","a","=","min_past","``","and","``","b","=","ts.shape","[","axis","]","-","min_future","``","."]
code: def _get_bounds(self, ts: np.ndarray) -> Tuple[int, int]:
      return (
          self.min_past,
          ts.shape[self.axis] - self.min_future,
      )
  
imports: from typing import Tuple
  import numpy
  from gluonts.dataset.stat import ScaleHistogram
  from gluonts.pydantic import BaseModel
label_desc: Use image node_id 1 for calling the InstanceSampler obj's underlying member method code with example usage: obj._get_bounds(ts) and returns: self,  unknown