code: def crps(self, x):
      gamma, b, knot_positions = self.gamma, self.b, self.knot_positions
  
      a_tilde = self.cdf(x)
  
      max_a_tilde_knots = torch.max(
          a_tilde.unsqueeze(-1), knot_positions
      )
  
      knots_cubed = torch.pow(knot_positions, 3.0)
      coeff = (
          (1.0 - knots_cubed) / 3.0
          - knot_positions
          - torch.square(max_a_tilde_knots)
          + 2 * max_a_tilde_knots * knot_positions
      )
  
      return (
          (2 * a_tilde - 1) * x
          + (1 - 2 * a_tilde) * gamma
          + (b * coeff).sum(-1)
      )
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/distributions/piecewise_linear.py
imports: import torch
  import torch.nn.functional
  from torch.distributions import constraints, NegativeBinomial, Poisson, Distribution, TransformedDistribution, AffineTransform
  from torch.distributions.utils import broadcast_all, lazy_property
  from .utils import broadcast_shape
label_desc: Use image node_id 8 for calling the PiecewiseLinear obj's underlying member method code with example usage: obj.crps(x) and returns: unknown