docstr: event_t is the solution to event_fn 
docstr_tok: ["event_t","is","the","solution","to","event_fn"]
code: def forward(ctx, func, event_fn, event_t, state_t):
      """event_t is the solution to event_fn"""
      ctx.func = func
      ctx.event_fn = event_fn
      ctx.save_for_backward(event_t, state_t)
      return event_t.detach(), state_t.detach()
  
file_path: torchdiffeq/torchdiffeq/_impl/odeint.py
imports: import torch
  from torch.autograd.functional import vjp
  from .dopri5 import Dopri5Solver
  from .bosh3 import Bosh3Solver
  from .adaptive_heun import AdaptiveHeunSolver
  from .fehlberg2 import Fehlberg2
  from .fixed_grid import Euler, Midpoint, Heun3, RK4
  from .fixed_adams import AdamsBashforth, AdamsBashforthMoulton
  from .dopri8 import Dopri8Solver
  from .scipy_wrapper import ScipyWrapperODESolver
  from .misc import _check_inputs, _flat_to_shape
  from .interp import _interp_evaluate
label_desc: Use image node_id 1 for calling the ImplicitFnGradientRerouting obj's underlying member method code with example usage: obj.forward(ctx, func, event_fn, event_t, state_t) and returns: event_t,  state_t