class_docstr: A wrapper for discrete schedule DDPM models that output eps (the predicted
  noise).
class_docstr_tok: ["A","wrapper","for","discrete","schedule","DDPM","models","that","output","eps","(","the","predicted","noise",")","."]
code: def __init__(self, model, quantize=False):
      alphas_cumprod = model.alphas_cumprod
      super().__init__(
          ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize
      )
      self.inner_model = model
      self.sigma_data = 1.0
  
imports: import math
  import torch
  import torchsde
  from scipy import integrate
  from torch import nn
  from torchdiffeq import odeint
  from tqdm.auto import tqdm, trange
label_desc: Use image node_id 1 to create a new DiscreteEpsDDPMDenoiser object from inherited base classes: DiscreteSchedule with example: obj = DiscreteEpsDDPMDenoiser(model, quantize)