code: def quantile(
      self,
      alpha: torch.Tensor,
      hidden_state: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      result = self.base_dist.quantile(alpha, hidden_state=hidden_state)
      result = result.reshape(
          self.base_dist.hidden_state.size(0),
          -1,
          self.base_dist.prediction_length,
      ).transpose(0, 1)
      for transform in self.transforms:
          # transform separate for each prediction horizon
          result = transform(result)
  
      return result.transpose(0, 1).reshape_as(alpha)
  
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 6 for calling the TransformedMQF2Distribution obj's underlying member method code with example usage: obj.quantile(alpha, hidden_state) and returns: result