class_docstr: Approximate function on tails based on knots and make a inference on query
  point. Can be used for either interpolation or extrapolation on tails.
  
  Parameters
  ----------
  x_coord
      x-coordinates of the data points must be in increasing order.
  y_coord
      y-coordinates of the data points - may be a higher numpy array.
  tol
      tolerance when performing the division and computing the log in the
      exponential extrapolation.
class_docstr_tok: ["Approximate","function","on","tails","based","on","knots","and","make","a","inference","on","query","point",".","Can","be","used","for","either","interpolation","or","extrapolation","on","tails",".","Parameters","--","--","--","--","--","x_coord","x-coordinates","of","the","data","points","must","be","in","increasing","order",".","y_coord","y-coordinates","of","the","data","points","-","may","be","a","higher","numpy","array",".","tol","tolerance","when","performing","the","division","and","computing","the","log","in","the","exponential","extrapolation","."]
docstr: Return an effective range of left and right tails.
code: def tail_range(self, default_left_tail=0.1, default_right_tail=0.9):
      """
      Return an effective range of left and right tails.
      """
      left_tail = max(
          self.x_coord[0],
          min(self.x_coord[1], default_left_tail),
      )
      right_tail = min(
          self.x_coord[-1],
          max(self.x_coord[-2], default_right_tail),
      )
      return left_tail, right_tail
  
imports: import re
  import logging
  from dataclasses import field
  from typing import Callable, Dict, List, Optional, Union, Tuple
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.pydantic import dataclass
  from gluonts import maybe
label_desc: Use image node_id 5 for calling the ExponentialTailApproximation obj's underlying member method code with example usage: obj.tail_range(default_left_tail, default_right_tail) and returns: left_tail,  right_tail