code: def log_prob(self, y: torch.Tensor) -> torch.Tensor:
      prediction_length = self.base_dist.prediction_length
  
      z, scale = self.scale_input(y)
      p = self.base_dist.log_prob(z)
  
      repeated_scale = self.repeat_scale(scale)
  
      # the log scale term can be omitted
      # in optimization because it is a constant
      # prediction_length is the dimension of each sample
      return p - prediction_length * torch.log(repeated_scale)
  
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 4 for calling the TransformedMQF2Distribution obj's underlying member method code with example usage: obj.log_prob(y) and returns: unknown