code: def backward(ctx, grad_t, grad_state):
      func = ctx.func
      event_fn = ctx.event_fn
      event_t, state_t = ctx.saved_tensors
  
      event_t = event_t.detach().clone().requires_grad_(True)
      state_t = state_t.detach().clone().requires_grad_(True)
  
      f_val = func(event_t, state_t)
  
      with torch.enable_grad():
          c, (par_dt, dstate) = vjp(event_fn, (event_t, state_t))
  
      # Total derivative of event_fn wrt t evaluated at event_t.
      dcdt = par_dt + torch.sum(dstate * f_val)
  
      # Add the gradient from final state to final time value as if a regular odeint was called.
      grad_t = grad_t + torch.sum(grad_state * f_val)
  
      dstate = dstate * (-grad_t / (dcdt + 1e-12)).reshape_as(c)
  
      grad_state = grad_state + dstate
  
      return None, None, None, grad_state
  
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 2 for calling the ImplicitFnGradientRerouting obj's underlying member method code with example usage: obj.backward(ctx, grad_t, grad_state) and returns: None,  None,  None,  grad_state