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.IntTensor,
  ) -> torch.FloatTensor:
      # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
      alphas_cumprod = self.alphas_cumprod.to(
          device=original_samples.device, dtype=original_samples.dtype
      )
      timesteps = timesteps.to(original_samples.device)
  
      sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
      sqrt_alpha_prod = sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
          sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
  
      sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
      sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape) < len(
          original_samples.shape
      ):
          sqrt_one_minus_alpha_prod = (
              sqrt_one_minus_alpha_prod.unsqueeze(-1)
          )
  
      noisy_samples = (
          sqrt_alpha_prod * original_samples
          + sqrt_one_minus_alpha_prod * noise
      )
      return noisy_samples
  
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