class_docstr: Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
  Langevin dynamics sampling.
  
  [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
  function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
  [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
  [`~SchedulerMixin.from_pretrained`] functions.
  
  For more details, see the original paper: https://arxiv.org/abs/2006.11239
  
  Args:
      scaler (`float`): ....
      s (`float`): ....
class_docstr_tok: ["Denoising","diffusion","probabilistic","models","(","DDPMs",")","explores","the","connections","between","denoising","score","matching","and","Langevin","dynamics","sampling",".","[","`","~ConfigMixin","`","]","takes","care","of","storing","all","config","attributes","that","are","passed","in","the","scheduler","'s","`","__init__","`","function",",","such","as","`","num_train_timesteps","`",".","They","can","be","accessed","via","`","scheduler.config.num_train_timesteps","`",".","[","`","SchedulerMixin","`","]","provides","general","loading","and","saving","functionality","via","the","[","`","SchedulerMixin.save_pretrained","`","]","and","[","`","~SchedulerMixin.from_pretrained","`","]","functions",".","For","more","details",",","see","the","original","paper",":","https",":","\/\/arxiv.org\/abs\/2006.11239","Args",":","scaler","(","`","float","`",")",":","....","s","(","`","float","`",")",":","...."]
code: def add_noise(
      self,
      original_samples: torch.FloatTensor,
      noise: torch.FloatTensor,
      timesteps: torch.FloatTensor,
  ) -> torch.FloatTensor:
      device = original_samples.device
      dtype = original_samples.dtype
      alpha_cumprod = self._alpha_cumprod(
          timesteps, device=device
      ).view(
          timesteps.size(0), *[1 for _ in original_samples.shape[1:]]
      )
      noisy_samples = (
          alpha_cumprod.sqrt() * original_samples
          + (1 - alpha_cumprod).sqrt() * noise
      )
      return noisy_samples.to(dtype=dtype)
  
imports: import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import torch
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from ..utils.torch_utils import randn_tensor
  from .scheduling_utils import SchedulerMixin
label_desc: Use image node_id 6 for calling the DDPMWuerstchenScheduler obj's underlying member method code with example usage: obj.add_noise(original_samples, noise, timesteps) and returns: noisy_samples