code: def __call__(
      self, x, timestep_index, text_embeddings, guidance_scale=8.0
  ):
      # the notation used in this paper is really confusing
      # gaussian noise input gets an index of M which maps
      # to t_0, i.e., reversed. so
      # t0->t1->t2 is M->M-1->M-2. in other words t_prev > t
  
      t = timestep_index.item() / self.num_train_timesteps
      t_prev = (
          timestep_index.item() + self.stride
      ) / self.num_train_timesteps
      t_prev_prev = (
          timestep_index.item() + 2 * self.stride
      ) / self.num_train_timesteps
  
      if timestep_index == self.timesteps[0]:
          # first step (xt0)
          self.q[t] = self.x_theta(
              x, t, text_embeddings, guidance_scale
          )
          return x  # typo in text, x0 in algorithm 2
  
      elif timestep_index == self.timesteps[1]:
          # second step (xt1)
          alpha_t, sigma_t, lambda_t = self.get_coeffs(t)
          _, sigma_t_prev, lambda_t_prev = self.get_coeffs(t_prev)
          h = lambda_t - lambda_t_prev
          x_tilde = (
              sigma_t / sigma_t_prev * x
              - alpha_t * torch.expm1(-h) * self.q[t_prev]
          )
          self.q[t] = self.x_theta(
              x_tilde, t, text_embeddings, guidance_scale
          )
          return x_tilde
  
      # 2 to M loop
      return self.dpm_solver_plus_plus_2m(
          x, t_prev_prev, t_prev, t, text_embeddings, guidance_scale
      )
  
file_path: diffusion-for-beginners/samplers/dpm_solver_plus_plus.py
imports: import torch
  import numpy
  import utils
label_desc: Use image node_id 5 for calling the DPMPlusPlusSampler obj's underlying member method code with example usage: obj.__call__(x, timestep_index, text_embeddings, guidance_scale) and returns: self, x, x_tilde