class_docstr: Distribution class for the Incremental (Spline) Quantile Function in the
  paper ``Learning Quantile Functions without Quantile Crossing for
  Distribution-free Time Series Forecasting``
  by Park, Robinson, Aubet, Kan, Gasthaus, Wang
  
  Parameters
  ----------
  spline_knots, spline_heights
      Tensor parametrizing the x-positions (y-positions) of the spline knots
      Shape: (*batch_shape, (num_qk-1), num_pieces)
  qk_x, qk_y
      Tensor containing the increasing x-positions (y-positions) of the
      quantile knots, Shape: (*batch_shape, num_qk)
  beta_l, beta_r
      Tensor containing the non-negative learnable parameter of the
      left (right) tail, Shape: (*batch_shape,)
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docstr: Computes the quantile level alpha_tilde such that
  alpha_tilde
  = q^{-1}(z) if z is in the tail region
  = qk_x_l or qk_x_r if z is in the non-tail region
  Parameters
  ----------
  z
      Observation, shape = (*batch_shape,)
  left_tail
      If True, compute alpha_tilde for the left tail
      Otherwise, compute alpha_tilde for the right tail
  Returns
  -------
  alpha_tilde
      Corresponding quantile level, shape = (*batch_shape,)
code: def cdf_tail(self, z: Tensor, left_tail: bool = True) -> Tensor:
      r"""
      Computes the quantile level alpha_tilde such that
      alpha_tilde
      = q^{-1}(z) if z is in the tail region
      = qk_x_l or qk_x_r if z is in the non-tail region
      Parameters
      ----------
      z
          Observation, shape = (*batch_shape,)
      left_tail
          If True, compute alpha_tilde for the left tail
          Otherwise, compute alpha_tilde for the right tail
      Returns
      -------
      alpha_tilde
          Corresponding quantile level, shape = (*batch_shape,)
      """
  
      F = self.F
  
      if left_tail:
          tail_a, tail_b, qk_x = self.tail_al, self.tail_bl, self.qk_x_l
      else:
          tail_a, tail_b, qk_x = (
              self.tail_ar,
              self.tail_br,
              1 - self.qk_x_r,
          )
  
      log_alpha_tilde = F.minimum((z - tail_b) / tail_a, F.log(qk_x))
      alpha_tilde = F.exp(log_alpha_tilde)
      return alpha_tilde if left_tail else 1 - alpha_tilde
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.util import cumsum
  from .bijection import AffineTransformation, Bijection
  from .distribution import Distribution, getF
  from .distribution_output import DistributionOutput
  from .transformed_distribution import TransformedDistribution
label_desc: Use image node_id 12 for calling the ISQF obj's underlying member method code with example usage: obj.cdf_tail(z, left_tail) and returns: unknown