code: def stochastic_sampler(
      self,
      x,
      i,
      encoder_hidden_states,
      s_churn=80,
      s_tmin=0.05,
      s_tmax=50,
      s_noise=1.003,
  ):
      # Algorithm 2 - arg defaults from table 5
      x_next, x_hat, t_next, t_hat, d = self.euler_method(
          x, i, encoder_hidden_states, s_churn, s_tmin, s_tmax, s_noise
      )  # first order denoising
      x_next = self.heuns_correction(
          x_next, x_hat, t_next, t_hat, d, encoder_hidden_states
      )  # second order correction
      return x_next
  
file_path: diffusion-for-beginners/samplers/heun.py
imports: import torch
  from math import sqrt
label_desc: Use image node_id 9 for calling the HeunSampler obj's underlying member method code with example usage: obj.stochastic_sampler(x, i, encoder_hidden_states, s_churn, s_tmin, s_tmax, s_noise) and returns: x_next