code: def crps(self, z: torch.Tensor) -> torch.Tensor:
      gamma = self.gamma
      b, knot_positions = self.b, self.knot_positions
  
      u_tilde = self.cdf(z)
  
      max_u_tilde_knots = torch.max(
          u_tilde.unsqueeze(-1), knot_positions
      )
  
      coeff = (
          (1 - knot_positions**3) / 3
          - knot_positions
          - max_u_tilde_knots**2
          + 2 * max_u_tilde_knots * knot_positions
      )
  
      result = (2 * u_tilde - 1) * (z - gamma) + torch.sum(
          b * coeff, dim=-1
      )
  
      return result
  
imports: from typing import Dict, List, Optional, Tuple, cast
  import torch
  import torch.nn.functional
  from torch.distributions import AffineTransform, TransformedDistribution
  from gluonts.core.component import validated
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 9 for calling the PiecewiseLinear obj's underlying member method code with example usage: obj.crps(z) and returns: result