code: def __init__(
      self,
      base_distribution: MQF2Distribution,
      transforms: List[AffineTransform],
      validate_args: bool = False,
  ) -> None:
      super().__init__(
          base_distribution, transforms, validate_args=validate_args
      )
  
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 1 to create a new TransformedMQF2Distribution object from inherited base classes: TransformedDistribution with example: obj = TransformedMQF2Distribution(base_distribution, transforms, validate_args)