code: def __init__(
      self,
      base_distribution: Distribution,
      loc: Optional[Tensor] = None,
      scale: Optional[Tensor] = None,
  ) -> None:
      super().__init__(
          base_distribution, [AffineTransformation(loc, scale)]
      )
  
      self.loc = loc if loc is not None else 0
      self.scale = scale if scale is not None else 1
  
imports: from typing import Any, List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet import autograd
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .None import bijection
  from .distribution import Distribution, _index_tensor, getF
  from .bijection import AffineTransformation
label_desc: Use image node_id 1 to create a new AffineTransformedDistribution object from inherited base classes: TransformedDistribution with example: obj = AffineTransformedDistribution(base_distribution, loc, scale)