code: def energy_score(self, y: torch.Tensor) -> torch.Tensor:
      beta = self.base_dist.beta
  
      z, scale = self.scale_input(y)
      loss = self.base_dist.energy_score(z)
  
      repeated_scale = self.repeat_scale(scale)
  
      return loss * (repeated_scale**beta)
  
imports: from typing import List, Optional, Tuple
  from cpflows.flows import DeepConvexFlow, SequentialFlow
  import torch
  from torch.distributions import AffineTransform, Distribution, Normal, TransformedDistribution
  import torch.nn.functional
label_desc: Use image node_id 5 for calling the TransformedMQF2Distribution obj's underlying member method code with example usage: obj.energy_score(y) and returns: unknown