class_docstr: wraps a discretizer, and prunes the sigmas
  params:
      strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
code: def __init__(
      self,
      discretization: Discretization,
      strength: float = 0.0,
      original_steps=None,
  ):
      self.discretization = discretization
      self.strength = strength
      self.original_steps = original_steps
      assert 0.0 <= self.strength <= 1.0
  
imports: import torch
  from sgm.modules.diffusionmodules.discretizer import Discretization
label_desc: Use image node_id 1 to create a new Txt2NoisyDiscretizationWrapper object  with example: obj = Txt2NoisyDiscretizationWrapper(discretization, strength, original_steps)