code: def _before_integrate(self, t):
      t0 = t[0]
      f0 = self.func(t[0], self.y0)
      if self.first_step is None:
          first_step = _select_initial_step(
              self.func,
              t[0],
              self.y0,
              self.order - 1,
              self.rtol,
              self.atol,
              self.norm,
              f0=f0,
          )
      else:
          first_step = self.first_step
      self.rk_state = _RungeKuttaState(
          self.y0, f0, t[0], t[0], first_step, [self.y0] * 5
      )
  
      # Handle step_t and jump_t arguments.
      if self.step_t is None:
          step_t = torch.tensor(
              [], dtype=self.dtype, device=self.y0.device
          )
      else:
          step_t = _sort_tvals(self.step_t, t0)
          step_t = step_t.to(self.dtype)
      if self.jump_t is None:
          jump_t = torch.tensor(
              [], dtype=self.dtype, device=self.y0.device
          )
      else:
          jump_t = _sort_tvals(self.jump_t, t0)
          jump_t = jump_t.to(self.dtype)
      counts = torch.cat([step_t, jump_t]).unique(return_counts=True)[1]
      if (counts > 1).any():
          raise ValueError(
              "`step_t` and `jump_t` must not have any repeated elements between them."
          )
  
      self.step_t = step_t
      self.jump_t = jump_t
      self.next_step_index = min(
          bisect.bisect(self.step_t.tolist(), t[0]),
          len(self.step_t) - 1,
      )
      self.next_jump_index = min(
          bisect.bisect(self.jump_t.tolist(), t[0]),
          len(self.jump_t) - 1,
      )
  
imports: import bisect
  import collections
  import torch
  from .event_handling import find_event
  from .interp import _interp_evaluate, _interp_fit
  from .misc import _compute_error_ratio, _select_initial_step, _optimal_step_size
  from .misc import Perturb
  from .solvers import AdaptiveStepsizeEventODESolver
label_desc: Use image node_id 3 for calling the RKAdaptiveStepsizeODESolver obj's underlying member method code with example usage: obj._before_integrate(t) without return types