code: def log_prob(self, value):
      # Assumes same distribution for all steps in the future, conditionally on the input data
      return -self.quantile_loss(
          self.predicted_quantiles, value, self.taus
      )
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/distributions/implicit_quantile.py
imports: import torch
  from torch.distributions import Distribution, TransformedDistribution, AffineTransform
label_desc: Use image node_id 3 for calling the ImplicitQuantile obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown