docstr: Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
  Args:
      x: A pytorch tensor. The initial value at time `s`.
      model_prev_list: A list of pytorch tensor. The previous computed model values.
      t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
      t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
      order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
      solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
          The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
  Returns:
      x_t: A pytorch tensor. The approximated solution at time `t`.
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code: def multistep_dpm_solver_update(
      self,
      x,
      model_prev_list,
      t_prev_list,
      t,
      order,
      solver_type="dpm_solver",
  ):
      """
      Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
      Args:
          x: A pytorch tensor. The initial value at time `s`.
          model_prev_list: A list of pytorch tensor. The previous computed model values.
          t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
          t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
          order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
          solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
              The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
      Returns:
          x_t: A pytorch tensor. The approximated solution at time `t`.
      """
      if order == 1:
          return self.dpm_solver_first_update(
              x, t_prev_list[-1], t, model_s=model_prev_list[-1]
          )
      elif order == 2:
          return self.multistep_dpm_solver_second_update(
              x,
              model_prev_list,
              t_prev_list,
              t,
              solver_type=solver_type,
          )
      elif order == 3:
          return self.multistep_dpm_solver_third_update(
              x,
              model_prev_list,
              t_prev_list,
              t,
              solver_type=solver_type,
          )
      else:
          raise ValueError(
              "Solver order must be 1 or 2 or 3, got {}".format(order)
          )
  
imports: import math
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 14 for calling the DPM_Solver obj's underlying member method code with example usage: obj.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, t, order, solver_type) and returns: self, self, self